CINXE.COM

<!DOCTYPE html> <html lang="en" xmlns:fb="http://www.facebook.com/2008/fbml" class="wf-loading"> <head prefix="og: https://ogp.me/ns# fb: https://ogp.me/ns/fb# academia: https://ogp.me/ns/fb/academia#"> <meta charset="utf-8"> <meta name=viewport content="width=device-width, initial-scale=1"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <title>Julien Gobeill | University of Applied Sciences Western Switzerland - Academia.edu</title>  <link href="//a.academia-assets.com/images/favicons/favicon-production.ico" rel="shortcut icon" type="image/vnd.microsoft.icon"> <link rel="apple-touch-icon" sizes="57x57" href="//a.academia-assets.com/images/favicons/apple-touch-icon-57x57.png"> <link rel="apple-touch-icon" sizes="60x60" href="//a.academia-assets.com/images/favicons/apple-touch-icon-60x60.png"> <link rel="apple-touch-icon" sizes="72x72" href="//a.academia-assets.com/images/favicons/apple-touch-icon-72x72.png"> <link rel="apple-touch-icon" sizes="76x76" href="//a.academia-assets.com/images/favicons/apple-touch-icon-76x76.png"> <link rel="apple-touch-icon" sizes="114x114" href="//a.academia-assets.com/images/favicons/apple-touch-icon-114x114.png"> <link rel="apple-touch-icon" sizes="120x120" href="//a.academia-assets.com/images/favicons/apple-touch-icon-120x120.png"> <link rel="apple-touch-icon" sizes="144x144" href="//a.academia-assets.com/images/favicons/apple-touch-icon-144x144.png"> <link rel="apple-touch-icon" sizes="152x152" href="//a.academia-assets.com/images/favicons/apple-touch-icon-152x152.png"> <link rel="apple-touch-icon" sizes="180x180" href="//a.academia-assets.com/images/favicons/apple-touch-icon-180x180.png"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/favicon-32x32.png" sizes="32x32"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/favicon-194x194.png" sizes="194x194"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/favicon-96x96.png" sizes="96x96"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/android-chrome-192x192.png" sizes="192x192"> <link rel="icon" type="image/png" href="//a.academia-assets.com/images/favicons/favicon-16x16.png" sizes="16x16"> <link rel="manifest" href="//a.academia-assets.com/images/favicons/manifest.json"> <meta name="msapplication-TileColor" content="#2b5797"> <meta name="msapplication-TileImage" content="//a.academia-assets.com/images/favicons/mstile-144x144.png"> <meta name="theme-color" content="#ffffff"> <script> window.performance && window.performance.measure && window.performance.measure("Time To First Byte", "requestStart", "responseStart"); </script> <script> (function() { if (!window.URLSearchParams || !window.history || !window.history.replaceState) { return; } var searchParams = new URLSearchParams(window.location.search); var paramsToDelete = [ 'fs', 'sm', 'swp', 'iid', 'nbs', 'rcc', // related content category 'rcpos', // related content carousel position 'rcpg', // related carousel page 'rchid', // related content hit id 'f_ri', // research interest id, for SEO tracking 'f_fri', // featured research interest, for SEO tracking (param key without value) 'f_rid', // from research interest directory for SEO tracking 'f_loswp', // from research interest pills on LOSWP sidebar for SEO tracking 'rhid', // referrring hit id ]; if (paramsToDelete.every((key) => searchParams.get(key) === null)) { return; } paramsToDelete.forEach((key) => { searchParams.delete(key); }); var cleanUrl = new URL(window.location.href); cleanUrl.search = searchParams.toString(); history.replaceState({}, document.title, cleanUrl); })(); </script> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "profiles/works", 'action': "summary", 'controller_action': 'profiles/works#summary', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script type="text/javascript"> window.sendUserTiming = function(timingName) { if (!(window.performance && window.performance.measure)) return; var entries = window.performance.getEntriesByName(timingName, "measure"); if (entries.length !== 1) return; var timingValue = Math.round(entries[0].duration); gtag('event', 'timing_complete', { name: timingName, value: timingValue, event_category: 'User-centric', }); }; window.sendUserTiming("Time To First Byte"); </script> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="hOvW5noMpZC8R3sL7LAL5TKeNY7KaEi2Iu6Hy3fapY4raXqSfow0ZoO9NOQeJ6SEoPaIWRIU53tgXouV4JQGVA" /> <link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/wow-3d36c19b4875b226bfed0fcba1dcea3f2fe61148383d97c0465c016b8c969290.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/social/home-79e78ce59bef0a338eb6540ec3d93b4a7952115b56c57f1760943128f4544d42.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/heading-95367dc03b794f6737f30123738a886cf53b7a65cdef98a922a98591d60063e3.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/button-bfbac2a470372e2f3a6661a65fa7ff0a0fbf7aa32534d9a831d683d2a6f9e01b.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/body-170d1319f0e354621e81ca17054bb147da2856ec0702fe440a99af314a6338c5.css" /><link crossorigin="" href="https://fonts.gstatic.com/" rel="preconnect" /><link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=Gupter:wght@400;500;700&family=IBM+Plex+Mono:wght@300;400&family=Material+Symbols+Outlined:opsz,wght,FILL,GRAD@20,400,0,0&display=swap" rel="stylesheet" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/common-2b6f90dbd75f5941bc38f4ad716615f3ac449e7398313bb3bc225fba451cd9fa.css" /> <meta name="author" content="julien gobeill" /> <meta name="description" content="Julien Gobeill, University of Applied Sciences Western Switzerland: 18 Followers, 11 Following, 115 Research papers. Research interests: Explanation,…" /> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs" /> <script> var $controller_name = 'works'; var $action_name = "summary"; var $rails_env = 'production'; var $app_rev = '075e914b9e16164113b5b9afd7238a56a7292942'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.Aedu = { hit_data: null }; window.Aedu.SiteStats = {"premium_universities_count":14035,"monthly_visitors":"102 million","monthly_visitor_count":102411812,"monthly_visitor_count_in_millions":102,"user_count":283253509,"paper_count":55203019,"paper_count_in_millions":55,"page_count":432000000,"page_count_in_millions":432,"pdf_count":16500000,"pdf_count_in_millions":16}; window.Aedu.serverRenderTime = new Date(1740035391000); window.Aedu.timeDifference = new Date().getTime() - 1740035391000; window.Aedu.isUsingCssV1 = false; window.Aedu.enableLocalization = true; window.Aedu.activateFullstory = false; window.Aedu.serviceAvailability = { status: {"attention_db":"on","bibliography_db":"on","contacts_db":"on","email_db":"on","indexability_db":"on","mentions_db":"on","news_db":"on","notifications_db":"on","offsite_mentions_db":"on","redshift":"on","redshift_exports_db":"on","related_works_db":"on","ring_db":"on","user_tests_db":"on"}, serviceEnabled: function(service) { return this.status[service] === "on"; }, readEnabled: function(service) { return this.serviceEnabled(service) || this.status[service] === "read_only"; }, }; window.Aedu.viewApmTrace = function() { // Check if x-apm-trace-id meta tag is set, and open the trace in APM // in a new window if it is. var apmTraceId = document.head.querySelector('meta[name="x-apm-trace-id"]'); if (apmTraceId) { var traceId = apmTraceId.content; // Use trace ID to construct URL, an example URL looks like: // https://app.datadoghq.com/apm/traces?query=trace_id%31298410148923562634 var apmUrl = 'https://app.datadoghq.com/apm/traces?query=trace_id%3A' + traceId; window.open(apmUrl, '_blank'); } }; </script>  <link href="https://fonts.googleapis.com/css?family=Roboto:100,100i,300,300i,400,400i,500,500i,700,700i,900,900i" rel="stylesheet"> <link rel="preload" href="//maxcdn.bootstrapcdn.com/font-awesome/4.3.0/css/font-awesome.min.css" as="style" onload="this.rel='stylesheet'"> <link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/libraries-a9675dcb01ec4ef6aa807ba772c7a5a00c1820d3ff661c1038a20f80d06bb4e4.css" /> <link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/academia-40698df34f913bd208bb70f09d2feb7c6286046250be17a4db35bba2c08b0e2f.css" /> <link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system_legacy-056a9113b9a0f5343d013b29ee1929d5a18be35fdcdceb616600b4db8bd20054.css" /> <script src="//a.academia-assets.com/assets/webpack_bundles/runtime-bundle-005434038af4252ca37c527588411a3d6a0eabb5f727fac83f8bbe7fd88d93bb.js"></script> <script src="//a.academia-assets.com/assets/webpack_bundles/webpack_libraries_and_infrequently_changed.wjs-bundle-6145545f49b709c1199990a76c559bd4c35429284884cbcb3cf7f1916215e941.js"></script> <script src="//a.academia-assets.com/assets/webpack_bundles/core_webpack.wjs-bundle-5e022a2ab081599fcedc76886fa95a606f8073416cae1641695a9906c9a80b81.js"></script> <script src="//a.academia-assets.com/assets/webpack_bundles/sentry.wjs-bundle-5fe03fddca915c8ba0f7edbe64c194308e8ce5abaed7bffe1255ff37549c4808.js"></script> <script> jade = window.jade || {}; jade.helpers = window.$h; jade._ = window._; </script>  <script id="tag-manager-head-root">(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer_old','GTM-5G9JF7Z');</script>  <script> window.gptadslots = []; window.googletag = window.googletag || {}; window.googletag.cmd = window.googletag.cmd || []; </script> <script type="text/javascript"> // TODO(jacob): This should be defined, may be rare load order problem. // Checking if null is just a quick fix, will default to en if unset. // Better fix is to run this immedietely after I18n is set. if (window.I18n != null) { I18n.defaultLocale = "en"; I18n.locale = "en"; I18n.fallbacks = true; } </script> <link rel="canonical" href="https://hes-so.academia.edu/JulienGobeill" /> </head>   <body class='c-profiles/works a-summary logged_out'>  <div id="fb-root"></div><script>window.fbAsyncInit = function() { FB.init({ appId: "2369844204", version: "v8.0", status: true, cookie: true, xfbml: true }); // Additional initialization code. if (window.InitFacebook) { // facebook.ts already loaded, set it up. window.InitFacebook(); } else { // Set a flag for facebook.ts to find when it loads. window.academiaAuthReadyFacebook = true; } };</script><script>window.fbAsyncLoad = function() { // Protection against double calling of this function if (window.FB) { return; } (function(d, s, id){ var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) {return;} js = d.createElement(s); js.id = id; js.src = "//connect.facebook.net/en_US/sdk.js"; fjs.parentNode.insertBefore(js, fjs); }(document, 'script', 'facebook-jssdk')); } if (!window.defer_facebook) { // Autoload if not deferred window.fbAsyncLoad(); } else { // Defer loading by 5 seconds setTimeout(function() { window.fbAsyncLoad(); }, 5000); }</script> <div id="google-root"></div><script>window.loadGoogle = function() { if (window.InitGoogle) { // google.ts already loaded, set it up. window.InitGoogle("331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b"); } else { // Set a flag for google.ts to use when it loads. window.GoogleClientID = "331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b"; } };</script><script>window.googleAsyncLoad = function() { // Protection against double calling of this function (function(d) { var js; var id = 'google-jssdk'; var ref = d.getElementsByTagName('script')[0]; if (d.getElementById(id)) { return; } js = d.createElement('script'); js.id = id; js.async = true; js.onload = loadGoogle; js.src = "https://accounts.google.com/gsi/client" ref.parentNode.insertBefore(js, ref); }(document)); } if (!window.defer_google) { // Autoload if not deferred window.googleAsyncLoad(); } else { // Defer loading by 5 seconds setTimeout(function() { window.googleAsyncLoad(); }, 5000); }</script> <div id="tag-manager-body-root">  <noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-5G9JF7Z" height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>   <script> window.addEventListener('load', function() { if (document.querySelector('input[name="commit"]')) { document.querySelector('input[name="commit"]').addEventListener('click', function() { gtag('event', 'click', { event_category: 'button', event_label: 'Log In' }) }) } }); </script> </div> <script>var _comscore = _comscore || []; _comscore.push({ c1: "2", c2: "26766707" }); (function() { var s = document.createElement("script"), el = document.getElementsByTagName("script")[0]; s.async = true; s.src = (document.location.protocol == "https:" ? "https://sb" : "http://b") + ".scorecardresearch.com/beacon.js"; el.parentNode.insertBefore(s, el); })();</script><img src="https://sb.scorecardresearch.com/p?c1=2&c2=26766707&cv=2.0&cj=1" style="position: absolute; visibility: hidden" /> <div id='react-modal'></div> <div class='DesignSystem'> <a class='u-showOnFocus' href='#site'> Skip to main content </a> </div> <div id="upgrade_ie_banner" style="display: none;"><p>Academia.edu no longer supports Internet Explorer.</p><p>To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to <a href="https://www.academia.edu/upgrade-browser">upgrade your browser</a>.</p></div><script>// Show this banner for all versions of IE if (!!window.MSInputMethodContext || /(MSIE)/.test(navigator.userAgent)) { document.getElementById('upgrade_ie_banner').style.display = 'block'; }</script> <div class="DesignSystem bootstrap ShrinkableNav"><div class="navbar navbar-default main-header"><div class="container-wrapper" id="main-header-container"><div class="container"><div class="navbar-header"><div class="nav-left-wrapper u-mt0x"><div class="nav-logo"><a data-main-header-link-target="logo_home" href="https://www.academia.edu/"><img class="visible-xs-inline-block" style="height: 24px;" alt="Academia.edu" src="//a.academia-assets.com/images/academia-logo-redesign-2015-A.svg" width="24" height="24" /><img width="145.2" height="18" class="hidden-xs" style="height: 24px;" alt="Academia.edu" src="//a.academia-assets.com/images/academia-logo-redesign-2015.svg" /></a></div><div class="nav-search"><div class="SiteSearch-wrapper select2-no-default-pills"><form class="js-SiteSearch-form DesignSystem" action="https://www.academia.edu/search" accept-charset="UTF-8" method="get"><i class="SiteSearch-icon fa fa-search u-fw700 u-positionAbsolute u-tcGrayDark"></i><input class="js-SiteSearch-form-input SiteSearch-form-input form-control" data-main-header-click-target="search_input" name="q" placeholder="Search" type="text" value="" /></form></div></div></div><div class="nav-right-wrapper pull-right"><ul class="NavLinks js-main-nav list-unstyled"><li class="NavLinks-link"><a class="js-header-login-url Button Button--inverseGray Button--sm u-mb4x" id="nav_log_in" rel="nofollow" href="https://www.academia.edu/login">Log In</a></li><li class="NavLinks-link u-p0x"><a class="Button Button--inverseGray Button--sm u-mb4x" rel="nofollow" href="https://www.academia.edu/signup">Sign Up</a></li></ul><button class="hidden-lg hidden-md hidden-sm u-ml4x navbar-toggle collapsed" data-target=".js-mobile-header-links" data-toggle="collapse" type="button"><span class="icon-bar"></span><span class="icon-bar"></span><span class="icon-bar"></span></button></div></div><div class="collapse navbar-collapse js-mobile-header-links"><ul class="nav navbar-nav"><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/login">Log In</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/signup">Sign Up</a></li><li class="u-borderColorGrayLight u-borderBottom1 js-mobile-nav-expand-trigger"><a href="#">more&nbsp<span class="caret"></span></a></li><li><ul class="js-mobile-nav-expand-section nav navbar-nav u-m0x collapse"><li class="u-borderColorGrayLight u-borderBottom1"><a rel="false" href="https://www.academia.edu/about">About</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/press">Press</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="false" href="https://www.academia.edu/documents">Papers</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/terms">Terms</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/privacy">Privacy</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/copyright">Copyright</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://www.academia.edu/hiring"><i class="fa fa-briefcase"></i> We're Hiring!</a></li><li class="u-borderColorGrayLight u-borderBottom1"><a rel="nofollow" href="https://support.academia.edu/hc/en-us"><i class="fa fa-question-circle"></i> Help Center</a></li><li class="js-mobile-nav-collapse-trigger u-borderColorGrayLight u-borderBottom1 dropup" style="display:none"><a href="#">less&nbsp<span class="caret"></span></a></li></ul></li></ul></div></div></div><script>(function(){ var $moreLink = $(".js-mobile-nav-expand-trigger"); var $lessLink = $(".js-mobile-nav-collapse-trigger"); var $section = $('.js-mobile-nav-expand-section'); $moreLink.click(function(ev){ ev.preventDefault(); $moreLink.hide(); $lessLink.show(); $section.collapse('show'); }); $lessLink.click(function(ev){ ev.preventDefault(); $moreLink.show(); $lessLink.hide(); $section.collapse('hide'); }); })() if ($a.is_logged_in() || false) { new Aedu.NavigationController({ el: '.js-main-nav', showHighlightedNotification: false }); } else { $(".js-header-login-url").attr("href", $a.loginUrlWithRedirect()); } Aedu.autocompleteSearch = new AutocompleteSearch({el: '.js-SiteSearch-form'});</script></div></div> <div id='site' class='fixed'> <div id="content" class="clearfix"> <script>document.addEventListener('DOMContentLoaded', function(){ var $dismissible = $(".dismissible_banner"); $dismissible.click(function(ev) { $dismissible.hide(); }); });</script> <script src="//a.academia-assets.com/assets/webpack_bundles/profile.wjs-bundle-8e43dbfb783947b05fb193bb4a981fdefb46b9285b0cade100b20d38357a3356.js" defer="defer"></script><script>$viewedUser = Aedu.User.set_viewed( {"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","photo":"/images/s65_no_pic.png","has_photo":false,"department":{"id":431227,"name":"Information Documentaire","url":"https://hes-so.academia.edu/Departments/Information_Documentaire/Documents","university":{"id":2396,"name":"University of Applied Sciences Western Switzerland","url":"https://hes-so.academia.edu/"}},"position":"Post-Doc","position_id":2,"is_analytics_public":false,"interests":[{"id":15087,"name":"Explanation","url":"https://www.academia.edu/Documents/in/Explanation"},{"id":6414,"name":"Decomposition","url":"https://www.academia.edu/Documents/in/Decomposition"},{"id":10501,"name":"Mental Representation and Content","url":"https://www.academia.edu/Documents/in/Mental_Representation_and_Content"},{"id":19117,"name":"Daniel Dennett","url":"https://www.academia.edu/Documents/in/Daniel_Dennett"},{"id":19773,"name":"Structuralism (Philosophy)","url":"https://www.academia.edu/Documents/in/Structuralism_Philosophy_"}]} ); if ($a.is_logged_in() && $viewedUser.is_current_user()) { $('body').addClass('profile-viewed-by-owner'); } $socialProfiles = []</script><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://hes-so.academia.edu/JulienGobeill","location":"/JulienGobeill","scheme":"https","host":"hes-so.academia.edu","port":null,"pathname":"/JulienGobeill","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="ProfileCheckPaperUpdate" data-props="{}" data-trace="false" data-dom-id="ProfileCheckPaperUpdate-react-component-67301f14-3232-40b9-8611-7a5b8c461868"></div> <div id="ProfileCheckPaperUpdate-react-component-67301f14-3232-40b9-8611-7a5b8c461868"></div> <div class="DesignSystem"><div class="onsite-ping" id="onsite-ping"></div></div><div class="profile-user-info DesignSystem"><div class="social-profile-container"><div class="left-panel-container"><div class="user-info-component-wrapper"><div class="user-summary-cta-container"><div class="user-summary-container"><div class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Julien Gobeill</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://hes-so.academia.edu/">University of Applied Sciences Western Switzerland</a>, <a class="u-tcGrayDarker" href="https://hes-so.academia.edu/Departments/Information_Documentaire/Documents">Information Documentaire</a>, <span class="u-tcGrayDarker">Post-Doc</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="Julien" data-follow-user-id="32463478" data-follow-user-source="profile_button" data-has-google="false"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">add</span>Follow</button><button class="ds2-5-button hidden profile-cta-button grow js-profile-unfollow-button" data-broccoli-component="user-info.unfollow-button" data-click-track="profile-user-info-unfollow-button" data-unfollow-user-id="32463478"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">done</span>Following</button></div></div><div class="user-stats-container"><a><div class="stat-container js-profile-followers"><p class="label">Followers</p><p class="data">18</p></div></a><a><div class="stat-container js-profile-followees" data-broccoli-component="user-info.followees-count" data-click-track="profile-expand-user-info-following"><p class="label">Following</p><p class="data">11</p></div></a><a><div class="stat-container js-profile-coauthors" data-broccoli-component="user-info.coauthors-count" data-click-track="profile-expand-user-info-coauthors"><p class="label">Co-authors</p><p class="data">11</p></div></a><div class="js-mentions-count-container" style="display: none;"><a href="/JulienGobeill/mentions"><div class="stat-container"><p class="label">Mentions</p><p class="data"></p></div></a></div><span><div class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="suggested-academics-container"><div class="suggested-academics--header"><p class="ds2-5-body-md-bold">Related Authors</p></div><ul class="suggested-user-card-list"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://cedarville.academia.edu/AdamMorrone"><img class="profile-avatar u-positionAbsolute" alt="Adam Morrone" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/10017490/3097637/3645570/s200_adam.morrone.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://cedarville.academia.edu/AdamMorrone">Adam Morrone</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Cedarville University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://isb-sib.academia.edu/PascaleGaudet"><img class="profile-avatar u-positionAbsolute" alt="Pascale Gaudet" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/416267/131757/151636/s200_pascale.gaudet.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://isb-sib.academia.edu/PascaleGaudet">Pascale Gaudet</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Swiss Institute of Bioinformatics</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://uni-nl.academia.edu/MiguelCardoso"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://uni-nl.academia.edu/MiguelCardoso">Miguel Cardoso</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">TU Delft</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/RobVanOmmering"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/RobVanOmmering">Rob van Ommering</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/HongfangLiu2"><img class="profile-avatar u-positionAbsolute" alt="Hongfang Liu" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/193940354/56506786/44710389/s200_hongfang.liu.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/HongfangLiu2">Hongfang Liu</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/RSchafer1"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/RSchafer1">R. Schafer</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://rmit.academia.edu/LawrenceCavedon"><img class="profile-avatar u-positionAbsolute" alt="Lawrence Cavedon" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/9459165/142614114/132118207/s200_lawrence.cavedon.jpeg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://rmit.academia.edu/LawrenceCavedon">Lawrence Cavedon</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">RMIT University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/DmitriyFradkin"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/DmitriyFradkin">Dmitriy Fradkin</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/SylvainPoux"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/SylvainPoux">Sylvain Poux</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/PRuch"><img class="profile-avatar u-positionAbsolute" alt="Patrick Ruch" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/32541866/15085370/15800714/s200_patrick.ruch.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/PRuch">Patrick Ruch</a></div></div></ul></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="32463478" href="https://www.academia.edu/Documents/in/Explanation"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://hes-so.academia.edu/JulienGobeill","location":"/JulienGobeill","scheme":"https","host":"hes-so.academia.edu","port":null,"pathname":"/JulienGobeill","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Explanation"]}" data-trace="false" data-dom-id="Pill-react-component-f2dfa2e1-01a4-4330-acdf-0aaf0ccac891"></div> <div id="Pill-react-component-f2dfa2e1-01a4-4330-acdf-0aaf0ccac891"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="32463478" href="https://www.academia.edu/Documents/in/Decomposition"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Decomposition"]}" data-trace="false" data-dom-id="Pill-react-component-02f4e5ee-b4cf-41c0-ba2a-56093b3284b7"></div> <div id="Pill-react-component-02f4e5ee-b4cf-41c0-ba2a-56093b3284b7"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="32463478" href="https://www.academia.edu/Documents/in/Mental_Representation_and_Content"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Mental Representation and Content"]}" data-trace="false" data-dom-id="Pill-react-component-2a7cfc12-52c6-4618-a5a3-ee63c1f5d7e9"></div> <div id="Pill-react-component-2a7cfc12-52c6-4618-a5a3-ee63c1f5d7e9"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="32463478" href="https://www.academia.edu/Documents/in/Daniel_Dennett"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Daniel Dennett"]}" data-trace="false" data-dom-id="Pill-react-component-a65b0e88-2335-407b-8694-7c5baa427857"></div> <div id="Pill-react-component-a65b0e88-2335-407b-8694-7c5baa427857"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="32463478" href="https://www.academia.edu/Documents/in/Structuralism_Philosophy_"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Structuralism (Philosophy)"]}" data-trace="false" data-dom-id="Pill-react-component-07001de5-72eb-4fbf-8014-1e265916c1a5"></div> <div id="Pill-react-component-07001de5-72eb-4fbf-8014-1e265916c1a5"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Julien Gobeill</h3></div><div class="js-work-strip profile--work_container" data-work-id="87932855"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/87932855/Variomes_a_high_recall_search_engine_to_support_the_curation_of_genomic_variants"><img alt="Research paper thumbnail of Variomes: a high recall search engine to support the curation of genomic variants" class="work-thumbnail" src="https://attachments.academia-assets.com/92021879/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/87932855/Variomes_a_high_recall_search_engine_to_support_the_curation_of_genomic_variants">Variomes: a high recall search engine to support the curation of genomic variants</a></div><div class="wp-workCard_item"><span>Bioinformatics</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Motivation Identification and interpretation of clinically actionable variants is a critical bott...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Motivation Identification and interpretation of clinically actionable variants is a critical bottleneck. Searching for evidence in the literature is mandatory according to ASCO/AMP/CAP practice guidelines; however, it is both labor-intensive and error-prone. We developed a system to perform triage of publications relevant to support an evidence-based decision. The system is also able to prioritize variants. Our system searches within pre-annotated collections such as MEDLINE and PubMed Central. Results We assess the search effectiveness of the system using three different experimental settings: literature triage; variant prioritization and comparison of Variomes with LitVar. Almost two-thirds of the publications returned in the top-5 are relevant for clinical decision-support. Our approach enabled identifying 81.8% of clinically actionable variants in the top-3. Variomes retrieves on average +21.3% more articles than LitVar and returns the same number of results or more results than...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="31758a58b667e374685f2a3958f558ea" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92021879,"asset_id":87932855,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92021879/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="87932855"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="87932855"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87932855; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=87932855]").text(description); $(".js-view-count[data-work-id=87932855]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 87932855; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='87932855']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "31758a58b667e374685f2a3958f558ea" } } $('.js-work-strip[data-work-id=87932855]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":87932855,"title":"Variomes: a high recall search engine to support the curation of genomic variants","internal_url":"https://www.academia.edu/87932855/Variomes_a_high_recall_search_engine_to_support_the_curation_of_genomic_variants","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"cTJRR0NKZWpoUHFheUkvQStyeU4xNVdTSTY0WHpIaGxheGlDOHhCMHhLZ1ZxVFViK0dEMFdZT3FORGdkRGdPdS0tRXNSM3ZMNUhyeDVlT0d0SnQvNlVLdz09--2acc6b77c2aba170fc94baa2eadecc734e5bb85c"},"attachments":[{"id":92021879,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92021879/thumbnails/1.jpg","file_name":"btac146.pdf","download_url":"https://www.academia.edu/attachments/92021879/download_file","bulk_download_file_name":"Variomes_a_high_recall_search_engine_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92021879/btac146-libre.pdf?1664975434=\u0026response-content-disposition=attachment%3B+filename%3DVariomes_a_high_recall_search_engine_to.pdf\u0026Expires=1740038824\u0026Signature=VwbCxjaldODWaynKfZ7kIJ5cA4morxEMu1m347UbYRFXCxXc4TMDRv35P9c~9zoKLVS75RWfCECqyQtwcEXgHLC4jYnuF4iS4HEoK4X0IEtfyqHLofRCek7CWElm860zAx~ZfI98iQ2WuIwiwLDmApsES949s-RDy2UjRi5t3HCZJIdE~mN7D9Kq2s3E6ya7Joh7~lOw3A8VEJrJNYtRFRglvP4F3SUP4OrqewX6JTEfnHG0~-FS3oEd8xs65~aEKBE8g3Bx0e0FacTakBHx~HJkSGxaqjBOJ~nO9BYSPVWKkHV~IoA21USTTxrWBU4HYZrVsLa7IIrtVvMSmPLDUA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":92021878,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92021878/thumbnails/1.jpg","file_name":"btac146.pdf","download_url":"https://www.academia.edu/attachments/92021878/download_file","bulk_download_file_name":"Variomes_a_high_recall_search_engine_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92021878/btac146-libre.pdf?1664975429=\u0026response-content-disposition=attachment%3B+filename%3DVariomes_a_high_recall_search_engine_to.pdf\u0026Expires=1740038824\u0026Signature=WO0L7zNA4a9I8mRcQGCZX4G6Bmo1RKixlq6ranbSua9TfQH9IHKzk~oGCkXfXAje6fgguRbK65-HJwKK2ONKlcc13~azOFRDTaqI7YJGAJuzdoiHxuWKLLv2n5YF1yF3lcFnFvx0ZcgUQfyZ0muj6kBWMdW9Gk-NXPW9qr7tTyXq6QeIPc-M5iACkPLpppi59UxozUgm91O5tYA0c4tT0J1t27-XnVCOdCXiSOT1e9tDFqWjL-BSyj~gaGP2c40VFBWZ088Wc2FN8aSHR5QFZIPuYAeKpxefyKKLFnD5mO6KaHNHHkU22qyGRZkXvmFVTqTXNnX1U0UzDfHgDfmR-A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157987"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/83157987/Abstract_Using_Discourse_Analysis_to_Improve_Text_Categorization_in_MEDLINE"><img alt="Research paper thumbnail of Abstract Using Discourse Analysis to Improve Text Categorization in MEDLINE" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/83157987/Abstract_Using_Discourse_Analysis_to_Improve_Text_Categorization_in_MEDLINE">Abstract Using Discourse Analysis to Improve Text Categorization in MEDLINE</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">studied in medical informatics in the context of the MEDLINE database, both for helping search in...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">studied in medical informatics in the context of the MEDLINE database, both for helping search in MEDLINE and in order to provide an indicative “gist ” of the content of an article. Automatic assignment of Medical Subject Headings (MeSH), which is formally an automatic text categorization task, has been proposed using different methods or combination of methods, including machine learning (naïve Bayes, neural networks…), linguistically-motivated methods (syntactic parsing, semantic tagging, or information retrieval. METHODS: In the present study, we propose to evaluate the impact of the argumentative structures of scientific articles to improve the categorization effectiveness of a categorizer, which combines linguistically-motivated and information retrieval methods. Our argumentative categorizer, which uses representation levels inherited from the field of discourse analysis, is able to classify sentences of an abstract in four classes: PURPOSE; METHODS; RESULTS and CONCLUSION. Fo...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157987"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157987"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157987; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157987]").text(description); $(".js-view-count[data-work-id=83157987]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157987; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157987']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=83157987]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157987,"title":"Abstract Using Discourse Analysis to Improve Text Categorization in MEDLINE","internal_url":"https://www.academia.edu/83157987/Abstract_Using_Discourse_Analysis_to_Improve_Text_Categorization_in_MEDLINE","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"QzRES2UzcDlFNHE1eGdLeldRc21DUUZRZWQxT0VvTWN0ekg2b2lHR09qOFJiRjlSUlJ1Uk0vS2ZldGN1RFNCWS0tQzR0aHdxUEZTV2ZwUy95MmQzOVNnZz09--4932755db40ceb0c823674d93c045d82d1d28f7e"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157929"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/83157929/Customizing_a_variant_annotation_support_tool"><img alt="Research paper thumbnail of Customizing a variant annotation-support tool" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/83157929/Customizing_a_variant_annotation_support_tool">Customizing a variant annotation-support tool</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157929"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157929"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157929; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157929]").text(description); $(".js-view-count[data-work-id=83157929]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157929; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157929']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=83157929]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157929,"title":"Customizing a variant annotation-support tool","internal_url":"https://www.academia.edu/83157929/Customizing_a_variant_annotation_support_tool","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157928"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/83157928/Instance_based_Learning_for_ICD10_Categorization"><img alt="Research paper thumbnail of Instance-based Learning for ICD10 Categorization" class="work-thumbnail" src="https://attachments.academia-assets.com/88602087/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/83157928/Instance_based_Learning_for_ICD10_Categorization">Instance-based Learning for ICD10 Categorization</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the framework of the CLEF 2018 eHealth campaign, we investigated an instance-based approach fo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the framework of the CLEF 2018 eHealth campaign, we investigated an instance-based approach for extracting ICD10 codes from death certificates. The 360,000 annotated sentences contained in the training data were indexed with a standard search engine. Then, the k-Nearest Neighbors (k-NN) generated out of an input sentence were exploited in order to infer potential codes, thanks to majority voting. Compared to a standard dictionary-based approach, this simple and robust k-Nearest Neighbors algorithms achieved remarkable good performances (F-Measure 0.79, +13% compared to our dictionary-based approach, +70% compared to the official baseline). This purely statistical approach uses no linguistic knowledge, and could a priori be applied to any language with similar performance levels. The combination of the k-NN with a dictionary-based approach is also a simple way to improve the categorization effectiveness of the system. The reported results are consistent with inter-rater agreements...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2e8a405c8dbb2b3eafcc8a37126e755d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602087,"asset_id":83157928,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602087/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157928"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157928"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157928; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157928]").text(description); $(".js-view-count[data-work-id=83157928]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157928; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157928']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2e8a405c8dbb2b3eafcc8a37126e755d" } } $('.js-work-strip[data-work-id=83157928]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157928,"title":"Instance-based Learning for ICD10 Categorization","internal_url":"https://www.academia.edu/83157928/Instance_based_Learning_for_ICD10_Categorization","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"cXVpVHQ1MUhvbGg1QUh3V2hIbHF4RmJJT1hROVBKc0R4UmhZR0t2WCt0SENIRGJyV3RqRVFBUE9XbFlhb0NhNC0tdHovK2tmT21NOWI3RmNyc0orTzZtdz09--bcfb4382ec0a0aac0772a497102a1e9dcbd29cf4"},"attachments":[{"id":88602087,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602087/thumbnails/1.jpg","file_name":"paper_149.pdf","download_url":"https://www.academia.edu/attachments/88602087/download_file","bulk_download_file_name":"Instance_based_Learning_for_ICD10_Catego.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602087/paper_149-libre.pdf?1657847386=\u0026response-content-disposition=attachment%3B+filename%3DInstance_based_Learning_for_ICD10_Catego.pdf\u0026Expires=1740038824\u0026Signature=URj2DuRRdqzeCDUWlScujucfrFMM5d-gzhuIs0GrHRquiGHJezXPnoAwFmwPE4NnyG8XIbI3~2u0KF2mv91n9FT-HctrbZ64o-wcwxCZ61R3c6vGcQp~4emeCUMuUJBOJnvObFXSv~gK4L4XolC65SzU4mehGCoLc9AWMt7PxwUNE73LvOjaqL4X6tiFzMYYCe6UcrG81X5j-It6JgVAzEUy2Tc5aRqxrbMRALhDwaf32yP91AfSSirInzoTDbJt-eOkJ8zQPTRhVAXUMD6Nn~fNwwoWN-opIC1wmq5oZOfyfP5szZu1HnECy~py5kPE71Jzs1fwlnaA3HlUvWi9nQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157927"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157927/SIB_Text_Mining_at_TREC_2017_Precision_Medicine_Track"><img alt="Research paper thumbnail of SIB Text Mining at TREC 2017 Precision Medicine Track" class="work-thumbnail" src="https://attachments.academia-assets.com/88602088/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157927/SIB_Text_Mining_at_TREC_2017_Precision_Medicine_Track">SIB Text Mining at TREC 2017 Precision Medicine Track</a></div><div class="wp-workCard_item"><span>F1000Research</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The TREC 2018 Precision Medicine Track largely repeats the structure and evaluation of the 2017 t...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The TREC 2018 Precision Medicine Track largely repeats the structure and evaluation of the 2017 track. The collection remains identical. Again, our team participated in the both tasks of the track: 1) retrieving scientific abstracts addressing relevant treatments for a given case and 2) retrieving clinical trials for which a patient is eligible. Regarding the retrieval of scientific abstracts, we queried all abstracts concerning one of the entities of the topic (i.e. the disease, the gene or the genetic variant) using various strategies (e.g. search in annotations of the collection, free text search using or not using synonyms, search in the MeSH terms, etc.). Then, for a given topic, the complete set of abstracts was based on the generation of different queries with decreasing levels of specificity. The idea was to start with a very specific query containing gene, disease and variant, from which less specific queries would be inferred. Abstracts were then re-ranked based on differe...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f99b4ba048b26d13228b1fe9ef249b6b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602088,"asset_id":83157927,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602088/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157927"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157927"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157927; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157927]").text(description); $(".js-view-count[data-work-id=83157927]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157927; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157927']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f99b4ba048b26d13228b1fe9ef249b6b" } } $('.js-work-strip[data-work-id=83157927]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157927,"title":"SIB Text Mining at TREC 2017 Precision Medicine Track","internal_url":"https://www.academia.edu/83157927/SIB_Text_Mining_at_TREC_2017_Precision_Medicine_Track","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"WkE2WjB1Tkc5U25ZZFAyU0E5NmxxVEFreDU0SGgzRjFHc0krTmI4MFlDWnZNa3d6aWkxUkJ4L1VkNE1vVVl3TC0tdnJrTm0zSVQyNEZNbGl4bTNHWTBVdz09--20dfacc385752a222299019e526c18a0ec332abb"},"attachments":[{"id":88602088,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602088/thumbnails/1.jpg","file_name":"Published_20version.pdf","download_url":"https://www.academia.edu/attachments/88602088/download_file","bulk_download_file_name":"SIB_Text_Mining_at_TREC_2017_Precision_M.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602088/Published_20version-libre.pdf?1657847384=\u0026response-content-disposition=attachment%3B+filename%3DSIB_Text_Mining_at_TREC_2017_Precision_M.pdf\u0026Expires=1740038824\u0026Signature=WxeYCMKdGMAJum9Ams9SGfV0t~M4yEC~8KgMx6mSkUTk7kPrgKFpiq9bQZ-au27OM6hG1cXGvSYa-deQFcbs~7HIZf11tCJDUj9Elr18CnKk7iTxANQbeZfVMXp8Ull-ZPyKBPjCakEUdwTqY5DHRlF5oTEamzQavgeTiVlGJ6SfMextQMrhSz0pfv1g2oYReN5f7V3vFeXLOpb9dMpJV4jAPc4tk-rJpqDB6mrAW9NpMqkNRpI2MZ9fjsxhn13UPiNVr0h1YYZPqKyraCc0rUOPvYua1Oh2XS0poecqQBMFBa2EpJpo1469zorwcbZhI4vaVBcEP4lGtSJPni8rgg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157926"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157926/Apprentissage_et_classification_automatiques_pour_am%C3%A9liorer_la_pertinence_d_un_corpus_d_articles"><img alt="Research paper thumbnail of Apprentissage et classification automatiques pour améliorer la pertinence d’un corpus d’articles" class="work-thumbnail" src="https://attachments.academia-assets.com/88602085/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157926/Apprentissage_et_classification_automatiques_pour_am%C3%A9liorer_la_pertinence_d_un_corpus_d_articles">Apprentissage et classification automatiques pour améliorer la pertinence d’un corpus d’articles</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Dans le cadre d’un projet étudiant le développement des politiques environnementales et climatiqu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Dans le cadre d’un projet étudiant le développement des politiques environnementales et climatiques sur les quatre dernières décennies, l’un des moyens envisagés par des chercheurs en sciences économiques est de construire puis exploiter un corpus d’articles de presse relatifs à cette thématique. La première année du projet s’est concentrée sur les seules archives du New York Times. Ce sont néanmoins 2,6 millions d’articles qui étaient à traiter – une masse trop importante pour l’homme. Des chercheurs en sciences de l’information et en fouille de texte ont donc été associés à cette tâche de recherche d’information. Dans un premier temps, les 2,6 millions d’articles ont été moissonnés depuis le Web, puis indexés dans un moteur de recherche. La conception d’une équation de recherche complexe a permis de sélectionner un corpus intermédiaire de 170 000 articles, dont la précision (taux d’articles pertinents) a été évaluée à 14%. Dans un deuxième temps, un algorithme d’apprentissage auto...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e0b188cd995c3f9dcbe4e5fb982b5816" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602085,"asset_id":83157926,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602085/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157926"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157926"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157926; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157926]").text(description); $(".js-view-count[data-work-id=83157926]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157926; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157926']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e0b188cd995c3f9dcbe4e5fb982b5816" } } $('.js-work-strip[data-work-id=83157926]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157926,"title":"Apprentissage et classification automatiques pour améliorer la pertinence d’un corpus d’articles","internal_url":"https://www.academia.edu/83157926/Apprentissage_et_classification_automatiques_pour_am%C3%A9liorer_la_pertinence_d_un_corpus_d_articles","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"NmFWMCtTbkNtU0dTais2eDUxN0dNaWg5OUgreUE2NGhjS2NOWDJmTmtDRGJKKzRwcHdtMHRrRTZmMmVuYnJNbC0tQ0Z4NlZUQnlMRVIrY2pHR3B6clZqUT09--95752ac2630a4799e7cc423b7ee0afe4ebdedc69"},"attachments":[{"id":88602085,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602085/thumbnails/1.jpg","file_name":"Gobeill_2018_apprentissage_et_classification.pdf","download_url":"https://www.academia.edu/attachments/88602085/download_file","bulk_download_file_name":"Apprentissage_et_classification_automati.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602085/Gobeill_2018_apprentissage_et_classification-libre.pdf?1657847388=\u0026response-content-disposition=attachment%3B+filename%3DApprentissage_et_classification_automati.pdf\u0026Expires=1740038824\u0026Signature=ARtCjG2M2IfdE9AxJ3yE8WAMTkwfmqSj5y7oRkxkcgcPA1WMDpMmfNFVn1KRrGaqbfwE1IpwJUs9hslldu-ugfErdMKxnWYkuQzbccpiX4w6YI~ttEno2xnqaQBKGH42WAjYBeMfDncOyeqfkGF4XSlckrEBugUPjpfFZ3mVU4yb1FMSleJqJc5PVCMXIevJRALWkfSPOLZnYtXMGtrOdjpIZNqcM5CXC8ChUPD~FQM1aQiVy0f9VYR~BxXT9NS90UJLDOQTYi6havQgZ2vPJ8FqWRx9aiGucAYWUuoGlJFx2jjBy~JvBppL6QLwOJFoJpQmjyWuhFAyvpLbmD1RFw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157925"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157925/Global_picture_of_OAI_PMH_repositories_through_the_analysis_of_6_key_open_archive_meta_catalogs"><img alt="Research paper thumbnail of Global picture of OAI-PMH repositories through the analysis of 6 key open archive meta-catalogs" class="work-thumbnail" src="https://attachments.academia-assets.com/88602089/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157925/Global_picture_of_OAI_PMH_repositories_through_the_analysis_of_6_key_open_archive_meta_catalogs">Global picture of OAI-PMH repositories through the analysis of 6 key open archive meta-catalogs</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">By the end of the late 90&#39;s the Open Archives Initiative needed direction to insure its impro...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">By the end of the late 90&#39;s the Open Archives Initiative needed direction to insure its improvement and thus, created the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) standard. The movement showed a rise in popularity, followed by a decline then a relative stabilization. This process was essentially a way to ensure the viability of open archive repositories. However, a meta-catalog containing an ensemble of repositories was never established, which lead to confusion of what could be found in said catalogs. This study ultimately aims to find out what repository content can be found and where with the use of the 6 key meta-catalogs. Although they undoubtedly have numerous limitations pertaining to the available data, this article seeks to compare the common data in each meta-catalog and estimates which repositories are found within them (with approx. less than 1% in common within the 6 meta-catalog). Decisively, this paper identifies the need to collate this...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a7919c72529350034bc782e3585fa901" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602089,"asset_id":83157925,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602089/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157925"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157925"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157925; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157925]").text(description); $(".js-view-count[data-work-id=83157925]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157925; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157925']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a7919c72529350034bc782e3585fa901" } } $('.js-work-strip[data-work-id=83157925]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157925,"title":"Global picture of OAI-PMH repositories through the analysis of 6 key open archive meta-catalogs","internal_url":"https://www.academia.edu/83157925/Global_picture_of_OAI_PMH_repositories_through_the_analysis_of_6_key_open_archive_meta_catalogs","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"amgyWnVQbXI4aEZ0ZDVxTG80b0RMQVRIMFk2K0pWeHVXcmdwWDNYVU91VzlFWEJ3SmRMK1hvK1ZVakl5STMzei0tdUtVUm4rU3lmTzRXd0hwNXJpNVBEUT09--1fcfc2c64f6635409f12bf89491f2da31521f683"},"attachments":[{"id":88602089,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602089/thumbnails/1.jpg","file_name":"1708.08669v1.pdf","download_url":"https://www.academia.edu/attachments/88602089/download_file","bulk_download_file_name":"Global_picture_of_OAI_PMH_repositories_t.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602089/1708.08669v1-libre.pdf?1657847398=\u0026response-content-disposition=attachment%3B+filename%3DGlobal_picture_of_OAI_PMH_repositories_t.pdf\u0026Expires=1740038824\u0026Signature=GvZw4tAqbq27Mo6lDMRx~LUDiPxHXKILoGhUxnWLlpBA6hgAaiTtsncdsN8-VjqIuwOhL0ciRfpJ7TeEW-LrNyy5FW8tkaZZfcexl9W0~VXCv9wgGQNYhF3Hfdvo3e4oIU4P0GQ048Qwb2fT926lXoimANlvHgo2BiwzOaOmaCAXF5nvlv3Thw-M19rw6U88EpUqkIMkgFcMS~p1DN5wlU~ei0KV8KT6YC5aS0G8P2qJtbqHQg97hLRokNKObaezZulJoJfs2CI8x1Nm6qZDh42VbyEwhFtxo3Uf84K~Olf-9dGgvqJr0pPw0OFE56qrmgjXvvmMtRs6-lDtvWWifw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157924"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157924/Designing_retrieval_models_to_contrast_precision_driven_ad_hoc_search_vs_recall_driven_treatment_extraction_in_Precision_Medicine"><img alt="Research paper thumbnail of Designing retrieval models to contrast precision-driven ad hoc search vs. recall-driven treatment extraction in Precision Medicine" class="work-thumbnail" src="https://attachments.academia-assets.com/88602086/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157924/Designing_retrieval_models_to_contrast_precision_driven_ad_hoc_search_vs_recall_driven_treatment_extraction_in_Precision_Medicine">Designing retrieval models to contrast precision-driven ad hoc search vs. recall-driven treatment extraction in Precision Medicine</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The TREC 2019 Precision Medicine Track repeats the general structure and evaluation of the 2018 t...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The TREC 2019 Precision Medicine Track repeats the general structure and evaluation of the 2018 track. Our team participated in both tasks of the track, relative to scientific abstracts and clinical trials. 40 topics where patient data are given (demographic data, disease, gene and genetic variant) were available for this competition. The aim was to retrieve scientific abstracts and clinical trials of interest regarding a topic, modelling the description of a clinical case. In the first task, we aim at retrieving scientific abstracts introducing some relevant treatments for a given case. Our system is first based on the collection of a large set of abstracts related to a particular case using various strategies such as search with keywords within abstracts, search with normalized entities within annotated abstracts and the linear combination of various queries. We then apply different strategies to re-rank the resulting scientific abstracts set. In particular, we tested two strategi...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d6d7e0512e2e29abb3e6acf244b3cdf0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602086,"asset_id":83157924,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602086/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157924"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157924"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157924; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157924]").text(description); $(".js-view-count[data-work-id=83157924]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157924; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157924']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d6d7e0512e2e29abb3e6acf244b3cdf0" } } $('.js-work-strip[data-work-id=83157924]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157924,"title":"Designing retrieval models to contrast precision-driven ad hoc search vs. recall-driven treatment extraction in Precision Medicine","internal_url":"https://www.academia.edu/83157924/Designing_retrieval_models_to_contrast_precision_driven_ad_hoc_search_vs_recall_driven_treatment_extraction_in_Precision_Medicine","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"cmMwRTR4d0xQRVZONkVUU20zbzFiRklNdzBESHZKMEJwSVBsSjBSZlVHWC9uZkhXbTBTbWVCV0pjTkZSd2VJSy0tY0ZWUGdFbUJmTjJiM2xUUW1EcGVCdz09--c6d741acc1ca456db8e285ba24353d5f60f70482"},"attachments":[{"id":88602086,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602086/thumbnails/1.jpg","file_name":"BITEM_PM.PM.pdf","download_url":"https://www.academia.edu/attachments/88602086/download_file","bulk_download_file_name":"Designing_retrieval_models_to_contrast_p.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602086/BITEM_PM.PM-libre.pdf?1657847396=\u0026response-content-disposition=attachment%3B+filename%3DDesigning_retrieval_models_to_contrast_p.pdf\u0026Expires=1740038824\u0026Signature=AteGENNvn49uZm1ItkvncQbGjkiZz2p0zA4-XaSoGVCeFkG4mAYrZzqqYW-YyNV-Xx3FXgdObhnxDvdWPLVt25dWfmdiO-zY1nfjkRwbO9RzrigprnvWedPDUscfG5zl6z3YTmOGGV0PNbbkq~ZQlUJdCtyzWXBcz5C4ZY-yxVA2~Dcaq6qV7JQrIccKA3unN0I-S9uHnjRVGMHLuWaWMylzXF8AHF~yvC~~3BP1A5u~2Vn-O41A~s0Aa545urTiJVBNkoFk5FbRxuCplqMPsK9oALcQuoL4omKWCEXC4xf0HicOx5aqfty6cuajFXyOFpZg3JaaMOLIzayi9eNccg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157901"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157901/UPCLASS_a_Deep_Learning_based_Classifier_for_UniProtKB_Entry_Publications"><img alt="Research paper thumbnail of UPCLASS: a Deep Learning-based Classifier for UniProtKB Entry Publications" class="work-thumbnail" src="https://attachments.academia-assets.com/88602084/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157901/UPCLASS_a_Deep_Learning_based_Classifier_for_UniProtKB_Entry_Publications">UPCLASS: a Deep Learning-based Classifier for UniProtKB Entry Publications</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the UniProt Knowledgebase (UniProtKB), publications providing evidence for a specific protein ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the UniProt Knowledgebase (UniProtKB), publications providing evidence for a specific protein annotation entry are organized across different categories, such as function, interaction and expression, based on the type of data they contain. To provide a systematic way of categorizing computationally mapped bibliography in UniProt, we investigate a Convolution Neural Network (CNN) model to classify publications with accession annotations according to UniProtKB categories. The main challenge to categorize publications at the accession annotation level is that the same publication can be annotated with multiple proteins, and thus be associated to different category sets according to the evidence provided for the protein. We propose a model that divides the document into parts containing and not containing evidence for the protein annotation. Then, we use these parts to create different feature sets for each accession and feed them to separate layers of the network. The CNN model achi...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8036418e76c1a9bc999f594b79181b27" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602084,"asset_id":83157901,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602084/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157901"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157901"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157901; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157901]").text(description); $(".js-view-count[data-work-id=83157901]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157901; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157901']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8036418e76c1a9bc999f594b79181b27" } } $('.js-work-strip[data-work-id=83157901]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157901,"title":"UPCLASS: a Deep Learning-based Classifier for UniProtKB Entry Publications","internal_url":"https://www.academia.edu/83157901/UPCLASS_a_Deep_Learning_based_Classifier_for_UniProtKB_Entry_Publications","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"c2hYc2lyejhiTjVjSVJDK0hzL2orZ1hZVnFDeGRwMXBzeVBGODJwVC9ULzkrWHYxMGI1SDJYS29KVi85WjlUNy0tN09BemlmVjg5SkljaTZBRHFXYnhYdz09--fd32dcecfdb3fd860b3309f76d4ec6fd1974771b"},"attachments":[{"id":88602084,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602084/thumbnails/1.jpg","file_name":"842062.full.pdf","download_url":"https://www.academia.edu/attachments/88602084/download_file","bulk_download_file_name":"UPCLASS_a_Deep_Learning_based_Classifier.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602084/842062.full-libre.pdf?1657847396=\u0026response-content-disposition=attachment%3B+filename%3DUPCLASS_a_Deep_Learning_based_Classifier.pdf\u0026Expires=1740038824\u0026Signature=ctcf9PuFsy9A~jbROM9BCvbb57bbOAWeS1rOWB5p8rrnoLw-m0TCMcoHC00~J4-GEKeelr7xOotCgaQGLEuKuok-hUIq6gBmiT9U~WjM3JmErh63zXbTDjuXXvAE1HFikcOkJAM5kDhex8wAXUO5TP4XnPziffXweYYc-d39q7e4RnR67gvp8uR6wYVXWthdAFAHFscSfpXQHa6PiVba7dR62gAJpVBzewm2NNR2Jv7t4OguywqSSTLIt4jbKaFjWudz9uOLjCoN1lZk4asi4tU-XTxoVVTjPcpfkDHcb1LKlc9PXs4rnKxniWyhC48D1~g~2e9T6zqpytV1RDTC4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157865"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157865/Overview_of_the_BioCreative_VI_text_mining_services_for_Kinome_Curation_Track"><img alt="Research paper thumbnail of Overview of the BioCreative VI text-mining services for Kinome Curation Track" class="work-thumbnail" src="https://attachments.academia-assets.com/88602057/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157865/Overview_of_the_BioCreative_VI_text_mining_services_for_Kinome_Curation_Track">Overview of the BioCreative VI text-mining services for Kinome Curation Track</a></div><div class="wp-workCard_item"><span>Database</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The text-mining services for kinome curation track, part of BioCreative VI, proposed a competitio...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The text-mining services for kinome curation track, part of BioCreative VI, proposed a competition to assess the effectiveness of text mining to perform literature triage. The track has exploited an unpublished curated data set from the neXtProt database. This data set contained comprehensive annotations for 300 human protein kinases. For a given protein and a given curation axis [diseases or gene ontology (GO) biological processes], participants' systems had to identify and rank relevant articles in a collection of 5.2 M MEDLINE citations (task 1) or 530 000 full-text articles (task 2). Explored strategies comprised named-entity recognition and machine-learning frameworks. For that latter approach, participants developed methods to derive a set of negative instances, as the databases typically do not store articles that were judged as irrelevant by curators. The supervised approaches proposed by the participating groups achieved significant improvements compared to the baseline established in a previous study and compared to a basic PubMed search.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e456548cbf977d78e0a2336c5a08382e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602057,"asset_id":83157865,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602057/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157865"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157865"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157865; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157865]").text(description); $(".js-view-count[data-work-id=83157865]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157865; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157865']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e456548cbf977d78e0a2336c5a08382e" } } $('.js-work-strip[data-work-id=83157865]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157865,"title":"Overview of the BioCreative VI text-mining services for Kinome Curation Track","internal_url":"https://www.academia.edu/83157865/Overview_of_the_BioCreative_VI_text_mining_services_for_Kinome_Curation_Track","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"M2NsbTcwZ2h5bUM1V2NHQnNzZlgvVHhqOVpldHVpbWtTWkhnaGlWeGVBV2JaQUxOcyszUWNhWnRXcTVGKzloVi0tT1Fxc3JYcDBUaWFsVHpWTTZHdHhUUT09--8f88a64eb902e23a4ae895d46c6b08ed47a2253f"},"attachments":[{"id":88602057,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602057/thumbnails/1.jpg","file_name":"bay104.pdf","download_url":"https://www.academia.edu/attachments/88602057/download_file","bulk_download_file_name":"Overview_of_the_BioCreative_VI_text_mini.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602057/bay104-libre.pdf?1657847390=\u0026response-content-disposition=attachment%3B+filename%3DOverview_of_the_BioCreative_VI_text_mini.pdf\u0026Expires=1740038824\u0026Signature=Q2cSOBuBISLPsVjXVAI5pr-62X0BMcx0wgP61N6TtizxfA-vU-PG8ncFaUXgQk6JZlxIBQLe8Cqo2q73nn22eMVRPXpdk2RRNvDMCbEyEF7wWWHsV5UspWUW78SKGDmZmECUu5DbD4uC-gzrPf2iTDTbaSgz7wGB6MlWsiRpjuwpBZui0JFSW2MA5n5rLUjT4F3tPFrbXRseBhEsB1E4-peZUhsnfJSzC2qb91kWl0-8PVTC0unnP4jZsYm-tEsltV-Of7qE2K88Ij6CHm-H~y8swiWrwTqT3xGlkno88fZr4YyebXz~Sy7QsyoHMvXBK1km23Mt0P6n9sgszi0nIA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157864"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157864/Improving_average_ranking_precision_in_user_searches_for_biomedical_research_datasets"><img alt="Research paper thumbnail of Improving average ranking precision in user searches for biomedical research datasets" class="work-thumbnail" src="https://attachments.academia-assets.com/88602052/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157864/Improving_average_ranking_precision_in_user_searches_for_biomedical_research_datasets">Improving average ranking precision in user searches for biomedical research datasets</a></div><div class="wp-workCard_item"><span>Database</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Availability of research datasets is keystone for health and life science study reproducibility a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorization method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries, and provided competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP, being þ22.3% higher than the median infAP of the participant's best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system's performance increasing our baseline up to þ5.0% and þ3.4% for the infAP and infNDCG metrics, respectively. The similarity measure algorithm showed robust performance in different training conditions, with small performance variations compared to the Divergence from Randomness framework. Finally, the result categorization did not have significant impact on the system's performance. We believe that our solution could be used to enhance biomedical dataset management systems. The use of data driven expansion methods, such as those based on word embeddings, could be an alternative to the complexity of biomedical terminologies. Nevertheless, due to the limited size of the assessment set, further experiments need to be performed to draw conclusive results.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b01f3aa95ac8967319126ca098c08f01" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602052,"asset_id":83157864,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602052/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157864"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157864"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157864; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157864]").text(description); $(".js-view-count[data-work-id=83157864]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157864; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157864']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b01f3aa95ac8967319126ca098c08f01" } } $('.js-work-strip[data-work-id=83157864]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157864,"title":"Improving average ranking precision in user searches for biomedical research datasets","internal_url":"https://www.academia.edu/83157864/Improving_average_ranking_precision_in_user_searches_for_biomedical_research_datasets","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill"},"attachments":[{"id":88602052,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602052/thumbnails/1.jpg","file_name":"bax083.pdf","download_url":"https://www.academia.edu/attachments/88602052/download_file","bulk_download_file_name":"Improving_average_ranking_precision_in_u.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602052/bax083-libre.pdf?1657847393=\u0026response-content-disposition=attachment%3B+filename%3DImproving_average_ranking_precision_in_u.pdf\u0026Expires=1739960627\u0026Signature=Oe8NCAeyLbOthPXa6IoW4OhLH4JHLt-f9pYQyt1YDF6gGHJQBHyVcOzh3NtlDbyecq-BAp7UAwSLbUZ2Ir-6ILnZ6M-FaOZywwXNokZJbxpjENqGX~XNuV7ohLS21tVzSmvECZ25oiE1TS6tkPc5rkGpEQHfVkNrjkbEx5dhtQsIVt1R51lbRqBimaSGJKBDTxWtJUb~XkgCZRRoSdsp59Xm0nFR7hulZ08bwRXaNP2xmIx9iJxczQ2xklXHKG86cLxahDvtXvHBz3uh9yyjrFDq8jgPnIm32ru2b2-cAZMr6xdn2T9PwJIVST7aOqj1Xhoe2ab6Ufzrhj5zy4N6HA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157863"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157863/Deep_Question_Answering_for_protein_annotation"><img alt="Research paper thumbnail of Deep Question Answering for protein annotation" class="work-thumbnail" src="https://attachments.academia-assets.com/88602051/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157863/Deep_Question_Answering_for_protein_annotation">Deep Question Answering for protein annotation</a></div><div class="wp-workCard_item"><span>Database</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Biomedical professionals have access to a huge amount of literature, but when they use a search e...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using stateof-the-art dictionary-and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a þ100% improvement for both recall and precision.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="86d37a28d7cc2d8ce6075f2d1234e4d3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602051,"asset_id":83157863,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602051/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157863"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157863"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157863; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157863]").text(description); $(".js-view-count[data-work-id=83157863]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157863; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157863']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "86d37a28d7cc2d8ce6075f2d1234e4d3" } } $('.js-work-strip[data-work-id=83157863]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157863,"title":"Deep Question Answering for protein annotation","internal_url":"https://www.academia.edu/83157863/Deep_Question_Answering_for_protein_annotation","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"TDEzNlhVTm5iSGtzck1PRkRVUWEwWmJIUGRrem1ZU2FXMjRoVS9lRVJYZUtqWWNkcnhGemJGSmZZVXE4K2Zhci0tellhUHdnSFgxU3JXdlJMTVBmQjJUZz09--dacc22bf878063f3804fdf9d2b8a848518828d35"},"attachments":[{"id":88602051,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602051/thumbnails/1.jpg","file_name":"f9e8f88f3e848374bcdb800cb5ed152acf99.pdf","download_url":"https://www.academia.edu/attachments/88602051/download_file","bulk_download_file_name":"Deep_Question_Answering_for_protein_anno.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602051/f9e8f88f3e848374bcdb800cb5ed152acf99-libre.pdf?1657847388=\u0026response-content-disposition=attachment%3B+filename%3DDeep_Question_Answering_for_protein_anno.pdf\u0026Expires=1740038824\u0026Signature=QfUrVo5k4AAruRXOZJVCcjw-CGVczt9wAXCNLI0XOz~3T2ZBk70-T0oUXNW7bndaqS~6OqU1Ms9RljSLPIlTGVOaABtNtk5zn~UcPD1zysUMA-QTh2LGMnePkuE76L-NcQj-gjkdQjpSILwITYQIU4NSHlkj-ZZJyw9BU9LpliTHryHYzXEQi9jiOgE6c5TFFC9bNBl~bsPf7YfoFulHQFg5aa2GuRPaR2sxj53eT1Ic8qSu69vNc5Z7hiJoy-ytVBNbhP9aw~fQUCq3YHuCHjkmYgWveoRUU1QY2fL45W5CkFYVt~YEA87Jb-wssNFUqD3zqIVguC8RE78i4fUPrg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157858"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157858/Khresmoi_Professional_Multilingual_Semantic_Search_for_Medical_Professionals"><img alt="Research paper thumbnail of Khresmoi Professional: Multilingual Semantic Search for Medical Professionals" class="work-thumbnail" src="https://attachments.academia-assets.com/88602048/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157858/Khresmoi_Professional_Multilingual_Semantic_Search_for_Medical_Professionals">Khresmoi Professional: Multilingual Semantic Search for Medical Professionals</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">There is increasing interest in and need for innovative solutions to medical search. In this pape...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">There is increasing interest in and need for innovative solutions to medical search. In this paper we present the EU-funded Khresmoi medical search and access system, currently in year 3 of 4 of development across 12 partners. The Khresmoi system uses a component-based architecture housed in the cloud to allow for the development of several innovative applications to support target users ¶ medical information needs. The Khresmoi search systems based on this architecture have been designed to support the multilingual and multimodal information needs of three target groups: the general public, general practitioners and consultant radiologists. In this paper we focus on the presentation of the systems to support the latter two groups using semantic, multilingual text and image-based (including 2D and 3D radiology images) search.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="37c574e39c98b97664203e32de8f2c72" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602048,"asset_id":83157858,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602048/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157858"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157858"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157858; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157858]").text(description); $(".js-view-count[data-work-id=83157858]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157858; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157858']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "37c574e39c98b97664203e32de8f2c72" } } $('.js-work-strip[data-work-id=83157858]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157858,"title":"Khresmoi Professional: Multilingual Semantic Search for Medical Professionals","internal_url":"https://www.academia.edu/83157858/Khresmoi_Professional_Multilingual_Semantic_Search_for_Medical_Professionals","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"Ti9PMTg1czBMd1lqdGlzWTZuTUkwdDNPc3FMak9pVE1iRlZNT1pjN0dhamo5V3h2OHU4dmpIK0pmU3ovb29mSC0tOTVIdnpPQ0h1R2FBd0ozeU52L0EyZz09--8e05124d4f7e83d13325b9a51f40781fd14deab1"},"attachments":[{"id":88602048,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602048/thumbnails/1.jpg","file_name":"2013-bystron-m6154648351926789425.pdf","download_url":"https://www.academia.edu/attachments/88602048/download_file","bulk_download_file_name":"Khresmoi_Professional_Multilingual_Seman.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602048/2013-bystron-m6154648351926789425-libre.pdf?1657847388=\u0026response-content-disposition=attachment%3B+filename%3DKhresmoi_Professional_Multilingual_Seman.pdf\u0026Expires=1740038824\u0026Signature=ccNx~Fl9sY-3B9qRl2GAu2OP-b19tBm~D1xgUqrZht~uuC0wb9SJMWO1ba8XqsYr4t5jf~vICYelqBKoRu9-v3bsVTPwr2mhEhTSmrBDP-tuzTh3pLrQX7eXEdbR~S4IeX3HOYFxd2gV8-WGevg0a4l5huJMrff3zqxzmOUf4sOBkkP1-F~YEvkDkEIcEvbQ0sON0Xr23CEkNpAXo3~x3EVl5beNKRZsLorNytUE-p7SXgLZZVeoCUC-3hAygxyDOz7p1mmorxroD1AtJQnlPIzTT8dI6suDnTgQZuVlMpNFlXZgpuae-D8c5rfQ4q4D-gttsR9-rxJYzNh3t08HsA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157839"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/83157839/Khresmoi_multilingual_semantic_search_of_medical_text_and_images"><img alt="Research paper thumbnail of Khresmoi-multilingual semantic search of medical text and images" class="work-thumbnail" src="https://attachments.academia-assets.com/88602038/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/83157839/Khresmoi_multilingual_semantic_search_of_medical_text_and_images">Khresmoi-multilingual semantic search of medical text and images</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="48dd5f41928f5bb30196353481c063ce" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602038,"asset_id":83157839,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602038/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157839"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157839"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157839; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157839]").text(description); $(".js-view-count[data-work-id=83157839]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157839; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157839']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "48dd5f41928f5bb30196353481c063ce" } } $('.js-work-strip[data-work-id=83157839]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157839,"title":"Khresmoi-multilingual semantic search of medical text and images","internal_url":"https://www.academia.edu/83157839/Khresmoi_multilingual_semantic_search_of_medical_text_and_images","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"aFRubmJTRUxuMTRaN3RjUjI3YWVQL1pieDlvNVBBeFhoRjQ3SGtzbENmKzh3QU9KcjY4OWRONmtuWkhWUnI2Zi0tV2FYc2FXWE9lSVRDb2FLNVRsTGlJZz09--3059378b032a6c1401058e34f35cc946f6c4af3e"},"attachments":[{"id":88602038,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602038/thumbnails/1.jpg","file_name":"D2_899_MEDINFO2013.pdf","download_url":"https://www.academia.edu/attachments/88602038/download_file","bulk_download_file_name":"Khresmoi_multilingual_semantic_search_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602038/D2_899_MEDINFO2013-libre.pdf?1657847407=\u0026response-content-disposition=attachment%3B+filename%3DKhresmoi_multilingual_semantic_search_of.pdf\u0026Expires=1740038824\u0026Signature=DcIDN1~GRp7czvQXzn141zT5Y9Ewy1lHWAxfKsjs33ZjqomXmlQEQ3mordnxN18-07IAqCSKfKu11U~0~U3OftwHwl7bXUGsOICmaDl~eI7DZOvcPd4fa0Wn~nZpI7GE5crX-XgcFZ6WGCUKGfEs7wRN7D~2OyMmeASQAvFGyigdw11DjsLqXakUPZdGfj-iffTTrwtYoD13oYscV631nbDROVlq0MyhFVjMdXELuIEtMwOs7NUJQ54EWr-SK9w0bOYcQeILrfFc2KxnN-VRM0l-SuC8w-Bt74T8hXsWhL2XJoX88ytjZna4f9OUMQFKH8KgpLydFio8Ubv6V02R6Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157824"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157824/Using_argumentation_to_extract_key_sentences_from_biomedical_abstracts"><img alt="Research paper thumbnail of Using argumentation to extract key sentences from biomedical abstracts" class="work-thumbnail" src="https://attachments.academia-assets.com/88602037/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157824/Using_argumentation_to_extract_key_sentences_from_biomedical_abstracts">Using argumentation to extract key sentences from biomedical abstracts</a></div><div class="wp-workCard_item"><span>International Journal of Medical Informatics</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Knowledge bases support multiple research e orts such as providing contextual information for bio...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Knowledge bases support multiple research e orts such as providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. Some knowledge bases are automatically constructed, but most are populated via some form of manual curation. Manual curation is time consuming and di cult to scale in the context of an increasing publication rate. A recently described "data programming" paradigm seeks to circumvent this arduous process by combining distant supervision with simple rules and heuristics written as labeling functions that can be automatically applied to inputs. Unfortunately writing useful label functions requires substantial error analysis and is a nontrivial task: in early e orts to use data programming we found that producing each label function could take a few days. Producing a biomedical knowledge base with multiple node and edge types could take hundreds or possibly thousands of label functions. In this paper we sought to evaluate the extent to which label functions could be re-used across edge types. We used a subset of Hetionet v1 that centered on disease, compound, and gene nodes to evaluate this approach. We compared a baseline distant supervision model with the same distant supervision resources added to edge-type-speci c label functions, edgetype-mismatch label functions, and all label functions. We con rmed that adding additional edge-typespeci c label functions improves performance. We also found that adding one or a few edge-typemismatch label functions nearly always improved performance. Adding a large number of edge-typemismatch label functions produce variable performance that depends on the edge type being predicted and the label function's edge type source. Lastly, we show that this approach, even on this subgraph of Hetionet, could add new edges to Hetionet v1 with high con dence. We expect that practical use of this strategy would include additional ltering and scoring methods which would further enhance precision. .</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e7ac100d8052a2a9daffef853c3dfcc8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602037,"asset_id":83157824,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602037/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157824"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157824"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157824; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157824]").text(description); $(".js-view-count[data-work-id=83157824]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157824; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157824']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e7ac100d8052a2a9daffef853c3dfcc8" } } $('.js-work-strip[data-work-id=83157824]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157824,"title":"Using argumentation to extract key sentences from biomedical abstracts","internal_url":"https://www.academia.edu/83157824/Using_argumentation_to_extract_key_sentences_from_biomedical_abstracts","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill"},"attachments":[{"id":88602037,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602037/thumbnails/1.jpg","file_name":"730085.full.pdf","download_url":"https://www.academia.edu/attachments/88602037/download_file","bulk_download_file_name":"Using_argumentation_to_extract_key_sente.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602037/730085.full-libre.pdf?1657847409=\u0026response-content-disposition=attachment%3B+filename%3DUsing_argumentation_to_extract_key_sente.pdf\u0026Expires=1739960627\u0026Signature=duFEZrh~qRYs2dEIaA6a~A-lcoDRcDaG2dy2GKSZoLYR4FwcYqtsmr55OzDhQqcpnVLLXOJFhU67zO14SeatPuU0rDIqNUjR2fSWHAKiRcS8Iywadb13Y3V62m7zoysJGBCsunGYdCUbftmB9n2d~CcofH7JhnoCLKPVM6R-9ROWgqqThV3rjKGxrW7N2I9r0mWC9t8NlrO4OR~Sy~jDwnSXEM9JFV3kXEmkQnngU3TOGMbHYBN3QCTNvzurmFReMXiXzW05Wgsz4AG4vgL0ktvFcQ-11p8ECS2TShE9es8K6bHtkLZnqf17jOeQYXMwaRvCa8EzJNH51Jdl~FdcWw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157798"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157798/Managing_the_data_deluge_data_driven"><img alt="Research paper thumbnail of Managing the data deluge: data-driven" class="work-thumbnail" src="https://attachments.academia-assets.com/88602008/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157798/Managing_the_data_deluge_data_driven">Managing the data deluge: data-driven</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The available curated data lag behind current biological knowledge contained in the literature. T...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The available curated data lag behind current biological knowledge contained in the literature. Text mining can assist biologists and curators to locate and access this knowledge, for instance by characterizing the functional profile of publications. Gene Ontology (GO) category assignment in free text already supports various applications, such as powering ontology-based search engines, finding curation-relevant articles (triage) or helping the curator to identify and encode functions. Popular text mining tools for GO classification are based on so called thesaurus-based-or dictionary-basedapproaches, which exploit similarities between the input text and GO terms themselves. But their effectiveness remains limited owing to the complex nature of GO terms, which rarely occur in text. In contrast, machine learning approaches exploit similarities between the input text and already curated instances contained in a knowledge base to infer a functional profile. GO Annotations (GOA) and MEDLINE make possible to exploit a growing amount of curated abstracts (97 000 in November 2012) for populating this knowledge base. Our study compares a state-of-the-art thesaurus-based system with a machine learning system (based on a k-Nearest Neighbours algorithm) for the task of proposing a functional profile for unseen MEDLINE abstracts, and shows how resources and performances have evolved. Systems are evaluated on their ability to propose for a given abstract the GO terms (2.8 on average) used for curation in GOA. We show that since 2006, although a massive effort was put into adding synonyms in GO (+300%), our thesaurus-based system effectiveness is rather constant, reaching from 0.28 to 0.31 for Recall at 20 (R20). In contrast, thanks to its knowledge base growth, our machine learning system has steadily improved, reaching from 0.38 in 2006 to 0.56 for R20 in 2012. Integrated in semi-automatic workflows or in fully automatic pipelines, such systems are more and more efficient to provide assistance to biologists.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="64666b96c02a7c2f083cbf395deef86f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602008,"asset_id":83157798,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602008/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157798"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157798"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157798; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157798]").text(description); $(".js-view-count[data-work-id=83157798]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157798; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157798']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "64666b96c02a7c2f083cbf395deef86f" } } $('.js-work-strip[data-work-id=83157798]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157798,"title":"Managing the data deluge: data-driven","internal_url":"https://www.academia.edu/83157798/Managing_the_data_deluge_data_driven","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill"},"attachments":[{"id":88602008,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602008/thumbnails/1.jpg","file_name":"download.pdf","download_url":"https://www.academia.edu/attachments/88602008/download_file","bulk_download_file_name":"Managing_the_data_deluge_data_driven.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602008/download-libre.pdf?1657847392=\u0026response-content-disposition=attachment%3B+filename%3DManaging_the_data_deluge_data_driven.pdf\u0026Expires=1739960627\u0026Signature=N0~vStN6b1mW2NdfLbaJc35S7qg1jwaUttycUl5712SQAbDUQJcKg7PNWuzlhAozO66~S7IyJIWiwXdS5izaFqXULCWlgWEKTMduZGh0m56IuyxH4w95-6yqM5AzI6T0FTc3Z9E-uXcdtp2F1Xb~Y7YPhhdPmvkutrNJ-xyjm9ZcoW05KSZYO46T4bVIvT2Aync9abqbgz8Af3gVh3fwXLhZ0aqCd5-oiOQMzNMBy7zCwI6ODx22wkhvlg5dxXuuoM2kIERqm4M2y4xZkzBAWMJUdKnInKN78oDuSpmnlyc7SBQajwChits0l7eDXykNlkRle5dzrNXLwS8zOBvRlQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78975880"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78975880/Variomes_a_high_recall_search_engine_to_support_the_curation_of_genomic_variants"><img alt="Research paper thumbnail of Variomes: a high recall search engine to support the curation of genomic variants" class="work-thumbnail" src="https://attachments.academia-assets.com/85852696/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78975880/Variomes_a_high_recall_search_engine_to_support_the_curation_of_genomic_variants">Variomes: a high recall search engine to support the curation of genomic variants</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Precision oncology relies on the use of treatments targeting specific genetic variants. However, ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Precision oncology relies on the use of treatments targeting specific genetic variants. However, identifying clinically actionable variants as well as relevant information likely to be used to treat a patient with a given cancer is a labor-intensive task, which includes searching the literature for a large set of variants. The lack of universally adopted standard nomenclature for variants requires the development of variant-specific literature search engines. We develop a system to perform triage of publications relevant to support an evidence-based decision. Together with providing a ranked list of articles for a given variant, the system is also able to prioritize variants, as found in a Variant Calling Format, assuming that the clinical actionability of a genetic variant is correlated with the volume of literature published about the variant. Our system searches within three pre-annotated document collections: MEDLINE abstracts, PubMed Central full-text articles and ClinicalTrial...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2b228e6930b2761949613232d1ac6997" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85852696,"asset_id":78975880,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85852696/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78975880"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78975880"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78975880; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78975880]").text(description); $(".js-view-count[data-work-id=78975880]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78975880; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78975880']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2b228e6930b2761949613232d1ac6997" } } $('.js-work-strip[data-work-id=78975880]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78975880,"title":"Variomes: a high recall search engine to support the curation of genomic variants","internal_url":"https://www.academia.edu/78975880/Variomes_a_high_recall_search_engine_to_support_the_curation_of_genomic_variants","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"ZlNhM2ZvcjcvMnRPODJ2Qm5qQWthMlljM2szNDMwQk5SVzdXUXZ2RVdQdVo5OHhVQlJGWnhCSDNIakp6eEQ0YS0tWUZzMmRsS0REZHc0YVFsYkNWTHd5dz09--2f44b04b5b090c660889e962455ed5d08d9dfafd"},"attachments":[{"id":85852696,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85852696/thumbnails/1.jpg","file_name":"2021.05.29.446224.full.pdf","download_url":"https://www.academia.edu/attachments/85852696/download_file","bulk_download_file_name":"Variomes_a_high_recall_search_engine_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85852696/2021.05.29.446224.full-libre.pdf?1652296553=\u0026response-content-disposition=attachment%3B+filename%3DVariomes_a_high_recall_search_engine_to.pdf\u0026Expires=1740038824\u0026Signature=E54rqfstkrsljzCJWwgbmZ73lpuySEauF7Eb-OPHrAaurx7bax2wHnWukjltMPqYES-aLLf6tnHlVS1kTTJK20VsS3cuUWPtSJnV8BicTGf8glprGsJw5Sr-P6XBYPkiu7WEtUpWMsTzfYtqUaqQWd0pb6mEmUoiYvgoa-soTUWeNQOOm0Z8-51TP~stSKOabY-VXIoenqeDYOaRNrtoxeLIZUz6rEB2WJDBLA9XPnIraozdGVsEI5Bv0piGHAJluHOsqbGpuli8xPZpK49bGy4rjMZZbM8TqCCgFlSy4m6ffS130ye0j2-JGPuQkAStKRO8NLmbdFOtaRjeou4tBQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="65163609"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/65163609/Rapport_de_fin_de_projet_de_lACI_Ecole_et_Sciences_Cognitives_Mod%C3%A9lisation_cognitive_d%C3%A9l%C3%A8ves_en_alg%C3%A8bre_et_construction_de_strat%C3%A9gies_denseignement_dans_un_contexte_technologique"><img alt="Research paper thumbnail of Rapport de fin de projet de l'ACI: Ecole et Sciences Cognitives-Modélisation cognitive d'élèves en algèbre et construction de stratégies d'enseignement dans un contexte technologique" class="work-thumbnail" src="https://attachments.academia-assets.com/76878977/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/65163609/Rapport_de_fin_de_projet_de_lACI_Ecole_et_Sciences_Cognitives_Mod%C3%A9lisation_cognitive_d%C3%A9l%C3%A8ves_en_alg%C3%A8bre_et_construction_de_strat%C3%A9gies_denseignement_dans_un_contexte_technologique">Rapport de fin de projet de l'ACI: Ecole et Sciences Cognitives-Modélisation cognitive d'élèves en algèbre et construction de stratégies d'enseignement dans un contexte technologique</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Modélisation cognitive d'élèves en algèbre et construction de stratégies d'enseignement dans un c...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Modélisation cognitive d'élèves en algèbre et construction de stratégies d'enseignement dans un contexte technologique</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2c644ce5bfddf3625bfd142b63d25ec4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":76878977,"asset_id":65163609,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/76878977/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="65163609"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="65163609"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 65163609; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=65163609]").text(description); $(".js-view-count[data-work-id=65163609]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 65163609; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='65163609']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2c644ce5bfddf3625bfd142b63d25ec4" } } $('.js-work-strip[data-work-id=65163609]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":65163609,"title":"Rapport de fin de projet de l'ACI: Ecole et Sciences Cognitives-Modélisation cognitive d'élèves en algèbre et construction de stratégies d'enseignement dans un contexte technologique","internal_url":"https://www.academia.edu/65163609/Rapport_de_fin_de_projet_de_lACI_Ecole_et_Sciences_Cognitives_Mod%C3%A9lisation_cognitive_d%C3%A9l%C3%A8ves_en_alg%C3%A8bre_et_construction_de_strat%C3%A9gies_denseignement_dans_un_contexte_technologique","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"ekZOVDdPNUFpb2lkdUE2L25BUDJBNS9Od213K2srZUIrNnBNOStPdFlEQ1dMR0pkbTVUdGJZZCswY2pmTG9hRi0tNXQ0MHR6dWdoUU8rUitIZjZBSzJxUT09--89a6266942acc9a3cb2c5e535d070c1aa90e03dd"},"attachments":[{"id":76878977,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/76878977/thumbnails/1.jpg","file_name":"CLLeib123.pdf","download_url":"https://www.academia.edu/attachments/76878977/download_file","bulk_download_file_name":"Rapport_de_fin_de_projet_de_lACI_Ecole_e.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/76878977/CLLeib123-libre.pdf?1639991206=\u0026response-content-disposition=attachment%3B+filename%3DRapport_de_fin_de_projet_de_lACI_Ecole_e.pdf\u0026Expires=1740038824\u0026Signature=A4aodA2IcOt0JiR8M8URctrsALwVk6SvgtkOZ2JXJcEvw9mOL7ro-y4M4rqEIrrhNk0KLF2q6pErgXTNMX19QJfEgmyG5IKZeEsNN3mUyCeYQ5iHr9mRg3dA0BbWMc0YE5AOhAL3yIaHYzGwp5CaqKTsH64CUCTk32Uu9Ax0PfEs5O-6W0GbJl1ntSKd5XGN2u7s7uxwHditqBHatLpmoJdN1bGVwdwcLjs4GfsUjSqAcYVvudy0Ux7s6AtQjYeANIpa6mhtnnnD651zt42nKNCPcP26eRi9heb~Mua7ITl2jRb5LeVxpAkH1Uykg6rkN4A8tS0ROwSJ0AvqAWOJoA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="55325216"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/55325216/SIB_Literature_Services_RESTful_customizable_search_engines_in_biomedical_literature_enriched_with_automatically_mapped_biomedical_concepts"><img alt="Research paper thumbnail of SIB Literature Services: RESTful customizable search engines in biomedical literature, enriched with automatically mapped biomedical concepts" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/55325216/SIB_Literature_Services_RESTful_customizable_search_engines_in_biomedical_literature_enriched_with_automatically_mapped_biomedical_concepts">SIB Literature Services: RESTful customizable search engines in biomedical literature, enriched with automatically mapped biomedical concepts</a></div><div class="wp-workCard_item"><span>Nucleic Acids Research</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thanks to recent efforts by the text mining community, biocurators have now access to plenty of g...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thanks to recent efforts by the text mining community, biocurators have now access to plenty of good tools and Web interfaces for identifying and visualizing biomedical entities in literature. Yet, many of these systems start with a PubMed query, which is limited by strong Boolean constraints. Some semantic search engines exploit entities for Information Retrieval, and/or deliver relevance-based ranked results. Yet, they are not designed for supporting a specific curation workflow, and allow very limited control on the search process. The Swiss Institute of Bioinformatics Literature Services (SIBiLS) provide personalized Information Retrieval in the biological literature. Indeed, SIBiLS allow fully customizable search in semantically enriched contents, based on keywords and/or mapped biomedical entities from a growing set of standardized and legacy vocabularies. The services have been used and favourably evaluated to assist the curation of genes and gene products, by delivering cust...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="55325216"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="55325216"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 55325216; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=55325216]").text(description); $(".js-view-count[data-work-id=55325216]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 55325216; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='55325216']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=55325216]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":55325216,"title":"SIB Literature Services: RESTful customizable search engines in biomedical literature, enriched with automatically mapped biomedical concepts","internal_url":"https://www.academia.edu/55325216/SIB_Literature_Services_RESTful_customizable_search_engines_in_biomedical_literature_enriched_with_automatically_mapped_biomedical_concepts","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"Sy9SVGVVR1M1cVY4NHpyd2hOQkxuQXU3UndnVmo1RTFaY0c2OEVDaHQxNFhzZnRiUkRwT0VkdnA0cHZvSTR3dC0tZzNaa2ViZmxFVVJOYU8wNTU3WWRBZz09--f5aadaedaafe56aa95422e0958bf0452baff9958"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="55325215"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/55325215/Triage_by_ranking_to_support_the_curation_of_protein_interactions"><img alt="Research paper thumbnail of Triage by ranking to support the curation of protein interactions" class="work-thumbnail" src="https://attachments.academia-assets.com/71248158/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/55325215/Triage_by_ranking_to_support_the_curation_of_protein_interactions">Triage by ranking to support the curation of protein interactions</a></div><div class="wp-workCard_item"><span>Database</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Today, molecular biology databases are the cornerstone of knowledge sharing for life and health s...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Today, molecular biology databases are the cornerstone of knowledge sharing for life and health sciences. The curation and maintenance of these resources are labour intensive. Although text mining is gaining impetus among curators, its integration in curation workflow has not yet been widely adopted. The Swiss Institute of Bioinformatics Text Mining and CALIPHO groups joined forces to design a new curation support system named nextA5. In this report, we explore the integration of novel triage services to support the curation of two types of biological data: protein-protein interactions (PPIs) and posttranslational modifications (PTMs). The recognition of PPIs and PTMs poses a special challenge, as it not only requires the identification of biological entities (proteins or residues), but also that of particular relationships (e.g. binding or position). These relationships cannot be described with onto-terminological descriptors such as the Gene Ontology for molecular functions, which makes the triage task more challenging. Prioritizing papers for these tasks thus requires the development of different approaches. In this report, we propose a new method to prioritize articles containing information specific to PPIs and PTMs. The new resources (RESTful APIs, semantically annotated MEDLINE library) enrich the neXtA5 platform. We tuned the article prioritization model on a set of 100 proteins previously annotated by the CALIPHO group. The effectiveness of the triage service was tested with a dataset of 200 annotated proteins. We defined two sets of descriptors to support automatic triage: the first set to enrich for papers with PPI data, and the second for PTMs. All occurrences of</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="90f919cbad32f279fb982c4e1655a7bf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":71248158,"asset_id":55325215,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/71248158/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="55325215"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="55325215"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 55325215; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=55325215]").text(description); $(".js-view-count[data-work-id=55325215]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 55325215; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='55325215']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "90f919cbad32f279fb982c4e1655a7bf" } } $('.js-work-strip[data-work-id=55325215]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":55325215,"title":"Triage by ranking to support the curation of protein interactions","internal_url":"https://www.academia.edu/55325215/Triage_by_ranking_to_support_the_curation_of_protein_interactions","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"QXJLUVVEQ3pMR1dtdWpLQnV0ekRvUm53OTQwK0hES2lZRFNDS2o2WTFLM3dCUXBGWlpPcWZHWlVnMTljUG5uQy0tTllPZ3JXcUVKeUlTTTgxaXhsUFdDZz09--08abf84a6e1f2de3055cea4319c4b6cb6c183549"},"attachments":[{"id":71248158,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/71248158/thumbnails/1.jpg","file_name":"d9940fc194a5a3866f625af3450ef1fd76f9.pdf","download_url":"https://www.academia.edu/attachments/71248158/download_file","bulk_download_file_name":"Triage_by_ranking_to_support_the_curatio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/71248158/d9940fc194a5a3866f625af3450ef1fd76f9-libre.pdf?1633417965=\u0026response-content-disposition=attachment%3B+filename%3DTriage_by_ranking_to_support_the_curatio.pdf\u0026Expires=1740038824\u0026Signature=OXDc8jKfN8W14X-GBuOUbC37eeS827izK4HUBOA2r4Xt7AcKn5G61AW9wcqxF40A~7cc1FL~pFPBZXT0EG~VIfS2BNbk6pImwjD0E2gwhttFfZ6ESJ95-peOAm63KjM-Px6xkzpR8Jag6-g221Rmd8hfknccrr2hEQZAAR3sRR5DSmtFdAvWymnXqQhep1JBebHJR2n2sj3gDC04WEcWVX7ubrHKKqkmUfbIQbM22TEldRaFBPMjcV6k6Y5g~87oqwH-LBFc9FjZ8pKvtduPsNIEzQodl~h9-~z5ig0I~9bHaP5kUsR4uUe9KpcZaU650AOZgZK5RePaeoj6Jsb4CQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="3080076" id="papers"><div class="js-work-strip profile--work_container" data-work-id="87932855"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/87932855/Variomes_a_high_recall_search_engine_to_support_the_curation_of_genomic_variants"><img alt="Research paper thumbnail of Variomes: a high recall search engine to support the curation of genomic variants" class="work-thumbnail" src="https://attachments.academia-assets.com/92021879/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/87932855/Variomes_a_high_recall_search_engine_to_support_the_curation_of_genomic_variants">Variomes: a high recall search engine to support the curation of genomic variants</a></div><div class="wp-workCard_item"><span>Bioinformatics</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Motivation Identification and interpretation of clinically actionable variants is a critical bott...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Motivation Identification and interpretation of clinically actionable variants is a critical bottleneck. Searching for evidence in the literature is mandatory according to ASCO/AMP/CAP practice guidelines; however, it is both labor-intensive and error-prone. We developed a system to perform triage of publications relevant to support an evidence-based decision. The system is also able to prioritize variants. Our system searches within pre-annotated collections such as MEDLINE and PubMed Central. Results We assess the search effectiveness of the system using three different experimental settings: literature triage; variant prioritization and comparison of Variomes with LitVar. Almost two-thirds of the publications returned in the top-5 are relevant for clinical decision-support. Our approach enabled identifying 81.8% of clinically actionable variants in the top-3. Variomes retrieves on average +21.3% more articles than LitVar and returns the same number of results or more results than...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="31758a58b667e374685f2a3958f558ea" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":92021879,"asset_id":87932855,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/92021879/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="87932855"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="87932855"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87932855; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=87932855]").text(description); $(".js-view-count[data-work-id=87932855]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 87932855; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='87932855']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "31758a58b667e374685f2a3958f558ea" } } $('.js-work-strip[data-work-id=87932855]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":87932855,"title":"Variomes: a high recall search engine to support the curation of genomic variants","internal_url":"https://www.academia.edu/87932855/Variomes_a_high_recall_search_engine_to_support_the_curation_of_genomic_variants","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"cTJRR0NKZWpoUHFheUkvQStyeU4xNVdTSTY0WHpIaGxheGlDOHhCMHhLZ1ZxVFViK0dEMFdZT3FORGdkRGdPdS0tRXNSM3ZMNUhyeDVlT0d0SnQvNlVLdz09--2acc6b77c2aba170fc94baa2eadecc734e5bb85c"},"attachments":[{"id":92021879,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92021879/thumbnails/1.jpg","file_name":"btac146.pdf","download_url":"https://www.academia.edu/attachments/92021879/download_file","bulk_download_file_name":"Variomes_a_high_recall_search_engine_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92021879/btac146-libre.pdf?1664975434=\u0026response-content-disposition=attachment%3B+filename%3DVariomes_a_high_recall_search_engine_to.pdf\u0026Expires=1740038824\u0026Signature=VwbCxjaldODWaynKfZ7kIJ5cA4morxEMu1m347UbYRFXCxXc4TMDRv35P9c~9zoKLVS75RWfCECqyQtwcEXgHLC4jYnuF4iS4HEoK4X0IEtfyqHLofRCek7CWElm860zAx~ZfI98iQ2WuIwiwLDmApsES949s-RDy2UjRi5t3HCZJIdE~mN7D9Kq2s3E6ya7Joh7~lOw3A8VEJrJNYtRFRglvP4F3SUP4OrqewX6JTEfnHG0~-FS3oEd8xs65~aEKBE8g3Bx0e0FacTakBHx~HJkSGxaqjBOJ~nO9BYSPVWKkHV~IoA21USTTxrWBU4HYZrVsLa7IIrtVvMSmPLDUA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":92021878,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/92021878/thumbnails/1.jpg","file_name":"btac146.pdf","download_url":"https://www.academia.edu/attachments/92021878/download_file","bulk_download_file_name":"Variomes_a_high_recall_search_engine_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/92021878/btac146-libre.pdf?1664975429=\u0026response-content-disposition=attachment%3B+filename%3DVariomes_a_high_recall_search_engine_to.pdf\u0026Expires=1740038824\u0026Signature=WO0L7zNA4a9I8mRcQGCZX4G6Bmo1RKixlq6ranbSua9TfQH9IHKzk~oGCkXfXAje6fgguRbK65-HJwKK2ONKlcc13~azOFRDTaqI7YJGAJuzdoiHxuWKLLv2n5YF1yF3lcFnFvx0ZcgUQfyZ0muj6kBWMdW9Gk-NXPW9qr7tTyXq6QeIPc-M5iACkPLpppi59UxozUgm91O5tYA0c4tT0J1t27-XnVCOdCXiSOT1e9tDFqWjL-BSyj~gaGP2c40VFBWZ088Wc2FN8aSHR5QFZIPuYAeKpxefyKKLFnD5mO6KaHNHHkU22qyGRZkXvmFVTqTXNnX1U0UzDfHgDfmR-A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157987"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/83157987/Abstract_Using_Discourse_Analysis_to_Improve_Text_Categorization_in_MEDLINE"><img alt="Research paper thumbnail of Abstract Using Discourse Analysis to Improve Text Categorization in MEDLINE" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/83157987/Abstract_Using_Discourse_Analysis_to_Improve_Text_Categorization_in_MEDLINE">Abstract Using Discourse Analysis to Improve Text Categorization in MEDLINE</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">studied in medical informatics in the context of the MEDLINE database, both for helping search in...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">studied in medical informatics in the context of the MEDLINE database, both for helping search in MEDLINE and in order to provide an indicative “gist ” of the content of an article. Automatic assignment of Medical Subject Headings (MeSH), which is formally an automatic text categorization task, has been proposed using different methods or combination of methods, including machine learning (naïve Bayes, neural networks…), linguistically-motivated methods (syntactic parsing, semantic tagging, or information retrieval. METHODS: In the present study, we propose to evaluate the impact of the argumentative structures of scientific articles to improve the categorization effectiveness of a categorizer, which combines linguistically-motivated and information retrieval methods. Our argumentative categorizer, which uses representation levels inherited from the field of discourse analysis, is able to classify sentences of an abstract in four classes: PURPOSE; METHODS; RESULTS and CONCLUSION. Fo...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157987"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157987"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157987; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157987]").text(description); $(".js-view-count[data-work-id=83157987]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157987; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157987']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=83157987]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157987,"title":"Abstract Using Discourse Analysis to Improve Text Categorization in MEDLINE","internal_url":"https://www.academia.edu/83157987/Abstract_Using_Discourse_Analysis_to_Improve_Text_Categorization_in_MEDLINE","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"QzRES2UzcDlFNHE1eGdLeldRc21DUUZRZWQxT0VvTWN0ekg2b2lHR09qOFJiRjlSUlJ1Uk0vS2ZldGN1RFNCWS0tQzR0aHdxUEZTV2ZwUy95MmQzOVNnZz09--4932755db40ceb0c823674d93c045d82d1d28f7e"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157929"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/83157929/Customizing_a_variant_annotation_support_tool"><img alt="Research paper thumbnail of Customizing a variant annotation-support tool" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/83157929/Customizing_a_variant_annotation_support_tool">Customizing a variant annotation-support tool</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157929"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157929"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157929; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157929]").text(description); $(".js-view-count[data-work-id=83157929]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157929; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157929']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=83157929]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157929,"title":"Customizing a variant annotation-support tool","internal_url":"https://www.academia.edu/83157929/Customizing_a_variant_annotation_support_tool","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157928"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/83157928/Instance_based_Learning_for_ICD10_Categorization"><img alt="Research paper thumbnail of Instance-based Learning for ICD10 Categorization" class="work-thumbnail" src="https://attachments.academia-assets.com/88602087/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/83157928/Instance_based_Learning_for_ICD10_Categorization">Instance-based Learning for ICD10 Categorization</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the framework of the CLEF 2018 eHealth campaign, we investigated an instance-based approach fo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the framework of the CLEF 2018 eHealth campaign, we investigated an instance-based approach for extracting ICD10 codes from death certificates. The 360,000 annotated sentences contained in the training data were indexed with a standard search engine. Then, the k-Nearest Neighbors (k-NN) generated out of an input sentence were exploited in order to infer potential codes, thanks to majority voting. Compared to a standard dictionary-based approach, this simple and robust k-Nearest Neighbors algorithms achieved remarkable good performances (F-Measure 0.79, +13% compared to our dictionary-based approach, +70% compared to the official baseline). This purely statistical approach uses no linguistic knowledge, and could a priori be applied to any language with similar performance levels. The combination of the k-NN with a dictionary-based approach is also a simple way to improve the categorization effectiveness of the system. The reported results are consistent with inter-rater agreements...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2e8a405c8dbb2b3eafcc8a37126e755d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602087,"asset_id":83157928,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602087/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157928"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157928"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157928; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157928]").text(description); $(".js-view-count[data-work-id=83157928]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157928; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157928']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2e8a405c8dbb2b3eafcc8a37126e755d" } } $('.js-work-strip[data-work-id=83157928]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157928,"title":"Instance-based Learning for ICD10 Categorization","internal_url":"https://www.academia.edu/83157928/Instance_based_Learning_for_ICD10_Categorization","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"cXVpVHQ1MUhvbGg1QUh3V2hIbHF4RmJJT1hROVBKc0R4UmhZR0t2WCt0SENIRGJyV3RqRVFBUE9XbFlhb0NhNC0tdHovK2tmT21NOWI3RmNyc0orTzZtdz09--bcfb4382ec0a0aac0772a497102a1e9dcbd29cf4"},"attachments":[{"id":88602087,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602087/thumbnails/1.jpg","file_name":"paper_149.pdf","download_url":"https://www.academia.edu/attachments/88602087/download_file","bulk_download_file_name":"Instance_based_Learning_for_ICD10_Catego.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602087/paper_149-libre.pdf?1657847386=\u0026response-content-disposition=attachment%3B+filename%3DInstance_based_Learning_for_ICD10_Catego.pdf\u0026Expires=1740038824\u0026Signature=URj2DuRRdqzeCDUWlScujucfrFMM5d-gzhuIs0GrHRquiGHJezXPnoAwFmwPE4NnyG8XIbI3~2u0KF2mv91n9FT-HctrbZ64o-wcwxCZ61R3c6vGcQp~4emeCUMuUJBOJnvObFXSv~gK4L4XolC65SzU4mehGCoLc9AWMt7PxwUNE73LvOjaqL4X6tiFzMYYCe6UcrG81X5j-It6JgVAzEUy2Tc5aRqxrbMRALhDwaf32yP91AfSSirInzoTDbJt-eOkJ8zQPTRhVAXUMD6Nn~fNwwoWN-opIC1wmq5oZOfyfP5szZu1HnECy~py5kPE71Jzs1fwlnaA3HlUvWi9nQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157927"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157927/SIB_Text_Mining_at_TREC_2017_Precision_Medicine_Track"><img alt="Research paper thumbnail of SIB Text Mining at TREC 2017 Precision Medicine Track" class="work-thumbnail" src="https://attachments.academia-assets.com/88602088/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157927/SIB_Text_Mining_at_TREC_2017_Precision_Medicine_Track">SIB Text Mining at TREC 2017 Precision Medicine Track</a></div><div class="wp-workCard_item"><span>F1000Research</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The TREC 2018 Precision Medicine Track largely repeats the structure and evaluation of the 2017 t...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The TREC 2018 Precision Medicine Track largely repeats the structure and evaluation of the 2017 track. The collection remains identical. Again, our team participated in the both tasks of the track: 1) retrieving scientific abstracts addressing relevant treatments for a given case and 2) retrieving clinical trials for which a patient is eligible. Regarding the retrieval of scientific abstracts, we queried all abstracts concerning one of the entities of the topic (i.e. the disease, the gene or the genetic variant) using various strategies (e.g. search in annotations of the collection, free text search using or not using synonyms, search in the MeSH terms, etc.). Then, for a given topic, the complete set of abstracts was based on the generation of different queries with decreasing levels of specificity. The idea was to start with a very specific query containing gene, disease and variant, from which less specific queries would be inferred. Abstracts were then re-ranked based on differe...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f99b4ba048b26d13228b1fe9ef249b6b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602088,"asset_id":83157927,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602088/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157927"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157927"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157927; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157927]").text(description); $(".js-view-count[data-work-id=83157927]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157927; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157927']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f99b4ba048b26d13228b1fe9ef249b6b" } } $('.js-work-strip[data-work-id=83157927]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157927,"title":"SIB Text Mining at TREC 2017 Precision Medicine Track","internal_url":"https://www.academia.edu/83157927/SIB_Text_Mining_at_TREC_2017_Precision_Medicine_Track","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"WkE2WjB1Tkc5U25ZZFAyU0E5NmxxVEFreDU0SGgzRjFHc0krTmI4MFlDWnZNa3d6aWkxUkJ4L1VkNE1vVVl3TC0tdnJrTm0zSVQyNEZNbGl4bTNHWTBVdz09--20dfacc385752a222299019e526c18a0ec332abb"},"attachments":[{"id":88602088,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602088/thumbnails/1.jpg","file_name":"Published_20version.pdf","download_url":"https://www.academia.edu/attachments/88602088/download_file","bulk_download_file_name":"SIB_Text_Mining_at_TREC_2017_Precision_M.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602088/Published_20version-libre.pdf?1657847384=\u0026response-content-disposition=attachment%3B+filename%3DSIB_Text_Mining_at_TREC_2017_Precision_M.pdf\u0026Expires=1740038824\u0026Signature=WxeYCMKdGMAJum9Ams9SGfV0t~M4yEC~8KgMx6mSkUTk7kPrgKFpiq9bQZ-au27OM6hG1cXGvSYa-deQFcbs~7HIZf11tCJDUj9Elr18CnKk7iTxANQbeZfVMXp8Ull-ZPyKBPjCakEUdwTqY5DHRlF5oTEamzQavgeTiVlGJ6SfMextQMrhSz0pfv1g2oYReN5f7V3vFeXLOpb9dMpJV4jAPc4tk-rJpqDB6mrAW9NpMqkNRpI2MZ9fjsxhn13UPiNVr0h1YYZPqKyraCc0rUOPvYua1Oh2XS0poecqQBMFBa2EpJpo1469zorwcbZhI4vaVBcEP4lGtSJPni8rgg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157926"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157926/Apprentissage_et_classification_automatiques_pour_am%C3%A9liorer_la_pertinence_d_un_corpus_d_articles"><img alt="Research paper thumbnail of Apprentissage et classification automatiques pour améliorer la pertinence d’un corpus d’articles" class="work-thumbnail" src="https://attachments.academia-assets.com/88602085/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157926/Apprentissage_et_classification_automatiques_pour_am%C3%A9liorer_la_pertinence_d_un_corpus_d_articles">Apprentissage et classification automatiques pour améliorer la pertinence d’un corpus d’articles</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Dans le cadre d’un projet étudiant le développement des politiques environnementales et climatiqu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Dans le cadre d’un projet étudiant le développement des politiques environnementales et climatiques sur les quatre dernières décennies, l’un des moyens envisagés par des chercheurs en sciences économiques est de construire puis exploiter un corpus d’articles de presse relatifs à cette thématique. La première année du projet s’est concentrée sur les seules archives du New York Times. Ce sont néanmoins 2,6 millions d’articles qui étaient à traiter – une masse trop importante pour l’homme. Des chercheurs en sciences de l’information et en fouille de texte ont donc été associés à cette tâche de recherche d’information. Dans un premier temps, les 2,6 millions d’articles ont été moissonnés depuis le Web, puis indexés dans un moteur de recherche. La conception d’une équation de recherche complexe a permis de sélectionner un corpus intermédiaire de 170 000 articles, dont la précision (taux d’articles pertinents) a été évaluée à 14%. Dans un deuxième temps, un algorithme d’apprentissage auto...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e0b188cd995c3f9dcbe4e5fb982b5816" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602085,"asset_id":83157926,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602085/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157926"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157926"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157926; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157926]").text(description); $(".js-view-count[data-work-id=83157926]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157926; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157926']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e0b188cd995c3f9dcbe4e5fb982b5816" } } $('.js-work-strip[data-work-id=83157926]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157926,"title":"Apprentissage et classification automatiques pour améliorer la pertinence d’un corpus d’articles","internal_url":"https://www.academia.edu/83157926/Apprentissage_et_classification_automatiques_pour_am%C3%A9liorer_la_pertinence_d_un_corpus_d_articles","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"NmFWMCtTbkNtU0dTais2eDUxN0dNaWg5OUgreUE2NGhjS2NOWDJmTmtDRGJKKzRwcHdtMHRrRTZmMmVuYnJNbC0tQ0Z4NlZUQnlMRVIrY2pHR3B6clZqUT09--95752ac2630a4799e7cc423b7ee0afe4ebdedc69"},"attachments":[{"id":88602085,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602085/thumbnails/1.jpg","file_name":"Gobeill_2018_apprentissage_et_classification.pdf","download_url":"https://www.academia.edu/attachments/88602085/download_file","bulk_download_file_name":"Apprentissage_et_classification_automati.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602085/Gobeill_2018_apprentissage_et_classification-libre.pdf?1657847388=\u0026response-content-disposition=attachment%3B+filename%3DApprentissage_et_classification_automati.pdf\u0026Expires=1740038824\u0026Signature=ARtCjG2M2IfdE9AxJ3yE8WAMTkwfmqSj5y7oRkxkcgcPA1WMDpMmfNFVn1KRrGaqbfwE1IpwJUs9hslldu-ugfErdMKxnWYkuQzbccpiX4w6YI~ttEno2xnqaQBKGH42WAjYBeMfDncOyeqfkGF4XSlckrEBugUPjpfFZ3mVU4yb1FMSleJqJc5PVCMXIevJRALWkfSPOLZnYtXMGtrOdjpIZNqcM5CXC8ChUPD~FQM1aQiVy0f9VYR~BxXT9NS90UJLDOQTYi6havQgZ2vPJ8FqWRx9aiGucAYWUuoGlJFx2jjBy~JvBppL6QLwOJFoJpQmjyWuhFAyvpLbmD1RFw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157925"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157925/Global_picture_of_OAI_PMH_repositories_through_the_analysis_of_6_key_open_archive_meta_catalogs"><img alt="Research paper thumbnail of Global picture of OAI-PMH repositories through the analysis of 6 key open archive meta-catalogs" class="work-thumbnail" src="https://attachments.academia-assets.com/88602089/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157925/Global_picture_of_OAI_PMH_repositories_through_the_analysis_of_6_key_open_archive_meta_catalogs">Global picture of OAI-PMH repositories through the analysis of 6 key open archive meta-catalogs</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">By the end of the late 90&#39;s the Open Archives Initiative needed direction to insure its impro...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">By the end of the late 90&#39;s the Open Archives Initiative needed direction to insure its improvement and thus, created the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) standard. The movement showed a rise in popularity, followed by a decline then a relative stabilization. This process was essentially a way to ensure the viability of open archive repositories. However, a meta-catalog containing an ensemble of repositories was never established, which lead to confusion of what could be found in said catalogs. This study ultimately aims to find out what repository content can be found and where with the use of the 6 key meta-catalogs. Although they undoubtedly have numerous limitations pertaining to the available data, this article seeks to compare the common data in each meta-catalog and estimates which repositories are found within them (with approx. less than 1% in common within the 6 meta-catalog). Decisively, this paper identifies the need to collate this...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a7919c72529350034bc782e3585fa901" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602089,"asset_id":83157925,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602089/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157925"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157925"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157925; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157925]").text(description); $(".js-view-count[data-work-id=83157925]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157925; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157925']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a7919c72529350034bc782e3585fa901" } } $('.js-work-strip[data-work-id=83157925]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157925,"title":"Global picture of OAI-PMH repositories through the analysis of 6 key open archive meta-catalogs","internal_url":"https://www.academia.edu/83157925/Global_picture_of_OAI_PMH_repositories_through_the_analysis_of_6_key_open_archive_meta_catalogs","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"amgyWnVQbXI4aEZ0ZDVxTG80b0RMQVRIMFk2K0pWeHVXcmdwWDNYVU91VzlFWEJ3SmRMK1hvK1ZVakl5STMzei0tdUtVUm4rU3lmTzRXd0hwNXJpNVBEUT09--1fcfc2c64f6635409f12bf89491f2da31521f683"},"attachments":[{"id":88602089,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602089/thumbnails/1.jpg","file_name":"1708.08669v1.pdf","download_url":"https://www.academia.edu/attachments/88602089/download_file","bulk_download_file_name":"Global_picture_of_OAI_PMH_repositories_t.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602089/1708.08669v1-libre.pdf?1657847398=\u0026response-content-disposition=attachment%3B+filename%3DGlobal_picture_of_OAI_PMH_repositories_t.pdf\u0026Expires=1740038824\u0026Signature=GvZw4tAqbq27Mo6lDMRx~LUDiPxHXKILoGhUxnWLlpBA6hgAaiTtsncdsN8-VjqIuwOhL0ciRfpJ7TeEW-LrNyy5FW8tkaZZfcexl9W0~VXCv9wgGQNYhF3Hfdvo3e4oIU4P0GQ048Qwb2fT926lXoimANlvHgo2BiwzOaOmaCAXF5nvlv3Thw-M19rw6U88EpUqkIMkgFcMS~p1DN5wlU~ei0KV8KT6YC5aS0G8P2qJtbqHQg97hLRokNKObaezZulJoJfs2CI8x1Nm6qZDh42VbyEwhFtxo3Uf84K~Olf-9dGgvqJr0pPw0OFE56qrmgjXvvmMtRs6-lDtvWWifw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157924"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157924/Designing_retrieval_models_to_contrast_precision_driven_ad_hoc_search_vs_recall_driven_treatment_extraction_in_Precision_Medicine"><img alt="Research paper thumbnail of Designing retrieval models to contrast precision-driven ad hoc search vs. recall-driven treatment extraction in Precision Medicine" class="work-thumbnail" src="https://attachments.academia-assets.com/88602086/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157924/Designing_retrieval_models_to_contrast_precision_driven_ad_hoc_search_vs_recall_driven_treatment_extraction_in_Precision_Medicine">Designing retrieval models to contrast precision-driven ad hoc search vs. recall-driven treatment extraction in Precision Medicine</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The TREC 2019 Precision Medicine Track repeats the general structure and evaluation of the 2018 t...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The TREC 2019 Precision Medicine Track repeats the general structure and evaluation of the 2018 track. Our team participated in both tasks of the track, relative to scientific abstracts and clinical trials. 40 topics where patient data are given (demographic data, disease, gene and genetic variant) were available for this competition. The aim was to retrieve scientific abstracts and clinical trials of interest regarding a topic, modelling the description of a clinical case. In the first task, we aim at retrieving scientific abstracts introducing some relevant treatments for a given case. Our system is first based on the collection of a large set of abstracts related to a particular case using various strategies such as search with keywords within abstracts, search with normalized entities within annotated abstracts and the linear combination of various queries. We then apply different strategies to re-rank the resulting scientific abstracts set. In particular, we tested two strategi...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d6d7e0512e2e29abb3e6acf244b3cdf0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602086,"asset_id":83157924,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602086/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157924"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157924"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157924; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157924]").text(description); $(".js-view-count[data-work-id=83157924]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157924; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157924']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d6d7e0512e2e29abb3e6acf244b3cdf0" } } $('.js-work-strip[data-work-id=83157924]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157924,"title":"Designing retrieval models to contrast precision-driven ad hoc search vs. recall-driven treatment extraction in Precision Medicine","internal_url":"https://www.academia.edu/83157924/Designing_retrieval_models_to_contrast_precision_driven_ad_hoc_search_vs_recall_driven_treatment_extraction_in_Precision_Medicine","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"cmMwRTR4d0xQRVZONkVUU20zbzFiRklNdzBESHZKMEJwSVBsSjBSZlVHWC9uZkhXbTBTbWVCV0pjTkZSd2VJSy0tY0ZWUGdFbUJmTjJiM2xUUW1EcGVCdz09--c6d741acc1ca456db8e285ba24353d5f60f70482"},"attachments":[{"id":88602086,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602086/thumbnails/1.jpg","file_name":"BITEM_PM.PM.pdf","download_url":"https://www.academia.edu/attachments/88602086/download_file","bulk_download_file_name":"Designing_retrieval_models_to_contrast_p.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602086/BITEM_PM.PM-libre.pdf?1657847396=\u0026response-content-disposition=attachment%3B+filename%3DDesigning_retrieval_models_to_contrast_p.pdf\u0026Expires=1740038824\u0026Signature=AteGENNvn49uZm1ItkvncQbGjkiZz2p0zA4-XaSoGVCeFkG4mAYrZzqqYW-YyNV-Xx3FXgdObhnxDvdWPLVt25dWfmdiO-zY1nfjkRwbO9RzrigprnvWedPDUscfG5zl6z3YTmOGGV0PNbbkq~ZQlUJdCtyzWXBcz5C4ZY-yxVA2~Dcaq6qV7JQrIccKA3unN0I-S9uHnjRVGMHLuWaWMylzXF8AHF~yvC~~3BP1A5u~2Vn-O41A~s0Aa545urTiJVBNkoFk5FbRxuCplqMPsK9oALcQuoL4omKWCEXC4xf0HicOx5aqfty6cuajFXyOFpZg3JaaMOLIzayi9eNccg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157901"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157901/UPCLASS_a_Deep_Learning_based_Classifier_for_UniProtKB_Entry_Publications"><img alt="Research paper thumbnail of UPCLASS: a Deep Learning-based Classifier for UniProtKB Entry Publications" class="work-thumbnail" src="https://attachments.academia-assets.com/88602084/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157901/UPCLASS_a_Deep_Learning_based_Classifier_for_UniProtKB_Entry_Publications">UPCLASS: a Deep Learning-based Classifier for UniProtKB Entry Publications</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the UniProt Knowledgebase (UniProtKB), publications providing evidence for a specific protein ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the UniProt Knowledgebase (UniProtKB), publications providing evidence for a specific protein annotation entry are organized across different categories, such as function, interaction and expression, based on the type of data they contain. To provide a systematic way of categorizing computationally mapped bibliography in UniProt, we investigate a Convolution Neural Network (CNN) model to classify publications with accession annotations according to UniProtKB categories. The main challenge to categorize publications at the accession annotation level is that the same publication can be annotated with multiple proteins, and thus be associated to different category sets according to the evidence provided for the protein. We propose a model that divides the document into parts containing and not containing evidence for the protein annotation. Then, we use these parts to create different feature sets for each accession and feed them to separate layers of the network. The CNN model achi...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8036418e76c1a9bc999f594b79181b27" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602084,"asset_id":83157901,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602084/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157901"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157901"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157901; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157901]").text(description); $(".js-view-count[data-work-id=83157901]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157901; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157901']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8036418e76c1a9bc999f594b79181b27" } } $('.js-work-strip[data-work-id=83157901]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157901,"title":"UPCLASS: a Deep Learning-based Classifier for UniProtKB Entry Publications","internal_url":"https://www.academia.edu/83157901/UPCLASS_a_Deep_Learning_based_Classifier_for_UniProtKB_Entry_Publications","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"c2hYc2lyejhiTjVjSVJDK0hzL2orZ1hZVnFDeGRwMXBzeVBGODJwVC9ULzkrWHYxMGI1SDJYS29KVi85WjlUNy0tN09BemlmVjg5SkljaTZBRHFXYnhYdz09--fd32dcecfdb3fd860b3309f76d4ec6fd1974771b"},"attachments":[{"id":88602084,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602084/thumbnails/1.jpg","file_name":"842062.full.pdf","download_url":"https://www.academia.edu/attachments/88602084/download_file","bulk_download_file_name":"UPCLASS_a_Deep_Learning_based_Classifier.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602084/842062.full-libre.pdf?1657847396=\u0026response-content-disposition=attachment%3B+filename%3DUPCLASS_a_Deep_Learning_based_Classifier.pdf\u0026Expires=1740038824\u0026Signature=ctcf9PuFsy9A~jbROM9BCvbb57bbOAWeS1rOWB5p8rrnoLw-m0TCMcoHC00~J4-GEKeelr7xOotCgaQGLEuKuok-hUIq6gBmiT9U~WjM3JmErh63zXbTDjuXXvAE1HFikcOkJAM5kDhex8wAXUO5TP4XnPziffXweYYc-d39q7e4RnR67gvp8uR6wYVXWthdAFAHFscSfpXQHa6PiVba7dR62gAJpVBzewm2NNR2Jv7t4OguywqSSTLIt4jbKaFjWudz9uOLjCoN1lZk4asi4tU-XTxoVVTjPcpfkDHcb1LKlc9PXs4rnKxniWyhC48D1~g~2e9T6zqpytV1RDTC4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157865"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157865/Overview_of_the_BioCreative_VI_text_mining_services_for_Kinome_Curation_Track"><img alt="Research paper thumbnail of Overview of the BioCreative VI text-mining services for Kinome Curation Track" class="work-thumbnail" src="https://attachments.academia-assets.com/88602057/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157865/Overview_of_the_BioCreative_VI_text_mining_services_for_Kinome_Curation_Track">Overview of the BioCreative VI text-mining services for Kinome Curation Track</a></div><div class="wp-workCard_item"><span>Database</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The text-mining services for kinome curation track, part of BioCreative VI, proposed a competitio...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The text-mining services for kinome curation track, part of BioCreative VI, proposed a competition to assess the effectiveness of text mining to perform literature triage. The track has exploited an unpublished curated data set from the neXtProt database. This data set contained comprehensive annotations for 300 human protein kinases. For a given protein and a given curation axis [diseases or gene ontology (GO) biological processes], participants' systems had to identify and rank relevant articles in a collection of 5.2 M MEDLINE citations (task 1) or 530 000 full-text articles (task 2). Explored strategies comprised named-entity recognition and machine-learning frameworks. For that latter approach, participants developed methods to derive a set of negative instances, as the databases typically do not store articles that were judged as irrelevant by curators. The supervised approaches proposed by the participating groups achieved significant improvements compared to the baseline established in a previous study and compared to a basic PubMed search.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e456548cbf977d78e0a2336c5a08382e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602057,"asset_id":83157865,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602057/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157865"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157865"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157865; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157865]").text(description); $(".js-view-count[data-work-id=83157865]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157865; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157865']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e456548cbf977d78e0a2336c5a08382e" } } $('.js-work-strip[data-work-id=83157865]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157865,"title":"Overview of the BioCreative VI text-mining services for Kinome Curation Track","internal_url":"https://www.academia.edu/83157865/Overview_of_the_BioCreative_VI_text_mining_services_for_Kinome_Curation_Track","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"M2NsbTcwZ2h5bUM1V2NHQnNzZlgvVHhqOVpldHVpbWtTWkhnaGlWeGVBV2JaQUxOcyszUWNhWnRXcTVGKzloVi0tT1Fxc3JYcDBUaWFsVHpWTTZHdHhUUT09--8f88a64eb902e23a4ae895d46c6b08ed47a2253f"},"attachments":[{"id":88602057,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602057/thumbnails/1.jpg","file_name":"bay104.pdf","download_url":"https://www.academia.edu/attachments/88602057/download_file","bulk_download_file_name":"Overview_of_the_BioCreative_VI_text_mini.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602057/bay104-libre.pdf?1657847390=\u0026response-content-disposition=attachment%3B+filename%3DOverview_of_the_BioCreative_VI_text_mini.pdf\u0026Expires=1740038824\u0026Signature=Q2cSOBuBISLPsVjXVAI5pr-62X0BMcx0wgP61N6TtizxfA-vU-PG8ncFaUXgQk6JZlxIBQLe8Cqo2q73nn22eMVRPXpdk2RRNvDMCbEyEF7wWWHsV5UspWUW78SKGDmZmECUu5DbD4uC-gzrPf2iTDTbaSgz7wGB6MlWsiRpjuwpBZui0JFSW2MA5n5rLUjT4F3tPFrbXRseBhEsB1E4-peZUhsnfJSzC2qb91kWl0-8PVTC0unnP4jZsYm-tEsltV-Of7qE2K88Ij6CHm-H~y8swiWrwTqT3xGlkno88fZr4YyebXz~Sy7QsyoHMvXBK1km23Mt0P6n9sgszi0nIA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157864"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157864/Improving_average_ranking_precision_in_user_searches_for_biomedical_research_datasets"><img alt="Research paper thumbnail of Improving average ranking precision in user searches for biomedical research datasets" class="work-thumbnail" src="https://attachments.academia-assets.com/88602052/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157864/Improving_average_ranking_precision_in_user_searches_for_biomedical_research_datasets">Improving average ranking precision in user searches for biomedical research datasets</a></div><div class="wp-workCard_item"><span>Database</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Availability of research datasets is keystone for health and life science study reproducibility a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorization method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries, and provided competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP, being þ22.3% higher than the median infAP of the participant's best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system's performance increasing our baseline up to þ5.0% and þ3.4% for the infAP and infNDCG metrics, respectively. The similarity measure algorithm showed robust performance in different training conditions, with small performance variations compared to the Divergence from Randomness framework. Finally, the result categorization did not have significant impact on the system's performance. We believe that our solution could be used to enhance biomedical dataset management systems. The use of data driven expansion methods, such as those based on word embeddings, could be an alternative to the complexity of biomedical terminologies. Nevertheless, due to the limited size of the assessment set, further experiments need to be performed to draw conclusive results.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b01f3aa95ac8967319126ca098c08f01" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602052,"asset_id":83157864,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602052/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157864"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157864"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157864; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157864]").text(description); $(".js-view-count[data-work-id=83157864]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157864; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157864']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b01f3aa95ac8967319126ca098c08f01" } } $('.js-work-strip[data-work-id=83157864]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157864,"title":"Improving average ranking precision in user searches for biomedical research datasets","internal_url":"https://www.academia.edu/83157864/Improving_average_ranking_precision_in_user_searches_for_biomedical_research_datasets","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill"},"attachments":[{"id":88602052,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602052/thumbnails/1.jpg","file_name":"bax083.pdf","download_url":"https://www.academia.edu/attachments/88602052/download_file","bulk_download_file_name":"Improving_average_ranking_precision_in_u.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602052/bax083-libre.pdf?1657847393=\u0026response-content-disposition=attachment%3B+filename%3DImproving_average_ranking_precision_in_u.pdf\u0026Expires=1739960627\u0026Signature=Oe8NCAeyLbOthPXa6IoW4OhLH4JHLt-f9pYQyt1YDF6gGHJQBHyVcOzh3NtlDbyecq-BAp7UAwSLbUZ2Ir-6ILnZ6M-FaOZywwXNokZJbxpjENqGX~XNuV7ohLS21tVzSmvECZ25oiE1TS6tkPc5rkGpEQHfVkNrjkbEx5dhtQsIVt1R51lbRqBimaSGJKBDTxWtJUb~XkgCZRRoSdsp59Xm0nFR7hulZ08bwRXaNP2xmIx9iJxczQ2xklXHKG86cLxahDvtXvHBz3uh9yyjrFDq8jgPnIm32ru2b2-cAZMr6xdn2T9PwJIVST7aOqj1Xhoe2ab6Ufzrhj5zy4N6HA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157863"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157863/Deep_Question_Answering_for_protein_annotation"><img alt="Research paper thumbnail of Deep Question Answering for protein annotation" class="work-thumbnail" src="https://attachments.academia-assets.com/88602051/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157863/Deep_Question_Answering_for_protein_annotation">Deep Question Answering for protein annotation</a></div><div class="wp-workCard_item"><span>Database</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Biomedical professionals have access to a huge amount of literature, but when they use a search e...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using stateof-the-art dictionary-and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a þ100% improvement for both recall and precision.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="86d37a28d7cc2d8ce6075f2d1234e4d3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602051,"asset_id":83157863,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602051/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157863"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157863"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157863; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157863]").text(description); $(".js-view-count[data-work-id=83157863]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157863; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157863']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "86d37a28d7cc2d8ce6075f2d1234e4d3" } } $('.js-work-strip[data-work-id=83157863]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157863,"title":"Deep Question Answering for protein annotation","internal_url":"https://www.academia.edu/83157863/Deep_Question_Answering_for_protein_annotation","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"TDEzNlhVTm5iSGtzck1PRkRVUWEwWmJIUGRrem1ZU2FXMjRoVS9lRVJYZUtqWWNkcnhGemJGSmZZVXE4K2Zhci0tellhUHdnSFgxU3JXdlJMTVBmQjJUZz09--dacc22bf878063f3804fdf9d2b8a848518828d35"},"attachments":[{"id":88602051,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602051/thumbnails/1.jpg","file_name":"f9e8f88f3e848374bcdb800cb5ed152acf99.pdf","download_url":"https://www.academia.edu/attachments/88602051/download_file","bulk_download_file_name":"Deep_Question_Answering_for_protein_anno.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602051/f9e8f88f3e848374bcdb800cb5ed152acf99-libre.pdf?1657847388=\u0026response-content-disposition=attachment%3B+filename%3DDeep_Question_Answering_for_protein_anno.pdf\u0026Expires=1740038824\u0026Signature=QfUrVo5k4AAruRXOZJVCcjw-CGVczt9wAXCNLI0XOz~3T2ZBk70-T0oUXNW7bndaqS~6OqU1Ms9RljSLPIlTGVOaABtNtk5zn~UcPD1zysUMA-QTh2LGMnePkuE76L-NcQj-gjkdQjpSILwITYQIU4NSHlkj-ZZJyw9BU9LpliTHryHYzXEQi9jiOgE6c5TFFC9bNBl~bsPf7YfoFulHQFg5aa2GuRPaR2sxj53eT1Ic8qSu69vNc5Z7hiJoy-ytVBNbhP9aw~fQUCq3YHuCHjkmYgWveoRUU1QY2fL45W5CkFYVt~YEA87Jb-wssNFUqD3zqIVguC8RE78i4fUPrg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157858"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157858/Khresmoi_Professional_Multilingual_Semantic_Search_for_Medical_Professionals"><img alt="Research paper thumbnail of Khresmoi Professional: Multilingual Semantic Search for Medical Professionals" class="work-thumbnail" src="https://attachments.academia-assets.com/88602048/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157858/Khresmoi_Professional_Multilingual_Semantic_Search_for_Medical_Professionals">Khresmoi Professional: Multilingual Semantic Search for Medical Professionals</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">There is increasing interest in and need for innovative solutions to medical search. In this pape...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">There is increasing interest in and need for innovative solutions to medical search. In this paper we present the EU-funded Khresmoi medical search and access system, currently in year 3 of 4 of development across 12 partners. The Khresmoi system uses a component-based architecture housed in the cloud to allow for the development of several innovative applications to support target users ¶ medical information needs. The Khresmoi search systems based on this architecture have been designed to support the multilingual and multimodal information needs of three target groups: the general public, general practitioners and consultant radiologists. In this paper we focus on the presentation of the systems to support the latter two groups using semantic, multilingual text and image-based (including 2D and 3D radiology images) search.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="37c574e39c98b97664203e32de8f2c72" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602048,"asset_id":83157858,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602048/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157858"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157858"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157858; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157858]").text(description); $(".js-view-count[data-work-id=83157858]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157858; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157858']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "37c574e39c98b97664203e32de8f2c72" } } $('.js-work-strip[data-work-id=83157858]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157858,"title":"Khresmoi Professional: Multilingual Semantic Search for Medical Professionals","internal_url":"https://www.academia.edu/83157858/Khresmoi_Professional_Multilingual_Semantic_Search_for_Medical_Professionals","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"Ti9PMTg1czBMd1lqdGlzWTZuTUkwdDNPc3FMak9pVE1iRlZNT1pjN0dhamo5V3h2OHU4dmpIK0pmU3ovb29mSC0tOTVIdnpPQ0h1R2FBd0ozeU52L0EyZz09--8e05124d4f7e83d13325b9a51f40781fd14deab1"},"attachments":[{"id":88602048,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602048/thumbnails/1.jpg","file_name":"2013-bystron-m6154648351926789425.pdf","download_url":"https://www.academia.edu/attachments/88602048/download_file","bulk_download_file_name":"Khresmoi_Professional_Multilingual_Seman.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602048/2013-bystron-m6154648351926789425-libre.pdf?1657847388=\u0026response-content-disposition=attachment%3B+filename%3DKhresmoi_Professional_Multilingual_Seman.pdf\u0026Expires=1740038824\u0026Signature=ccNx~Fl9sY-3B9qRl2GAu2OP-b19tBm~D1xgUqrZht~uuC0wb9SJMWO1ba8XqsYr4t5jf~vICYelqBKoRu9-v3bsVTPwr2mhEhTSmrBDP-tuzTh3pLrQX7eXEdbR~S4IeX3HOYFxd2gV8-WGevg0a4l5huJMrff3zqxzmOUf4sOBkkP1-F~YEvkDkEIcEvbQ0sON0Xr23CEkNpAXo3~x3EVl5beNKRZsLorNytUE-p7SXgLZZVeoCUC-3hAygxyDOz7p1mmorxroD1AtJQnlPIzTT8dI6suDnTgQZuVlMpNFlXZgpuae-D8c5rfQ4q4D-gttsR9-rxJYzNh3t08HsA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157839"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/83157839/Khresmoi_multilingual_semantic_search_of_medical_text_and_images"><img alt="Research paper thumbnail of Khresmoi-multilingual semantic search of medical text and images" class="work-thumbnail" src="https://attachments.academia-assets.com/88602038/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/83157839/Khresmoi_multilingual_semantic_search_of_medical_text_and_images">Khresmoi-multilingual semantic search of medical text and images</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="48dd5f41928f5bb30196353481c063ce" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602038,"asset_id":83157839,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602038/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157839"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157839"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157839; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157839]").text(description); $(".js-view-count[data-work-id=83157839]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157839; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157839']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "48dd5f41928f5bb30196353481c063ce" } } $('.js-work-strip[data-work-id=83157839]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157839,"title":"Khresmoi-multilingual semantic search of medical text and images","internal_url":"https://www.academia.edu/83157839/Khresmoi_multilingual_semantic_search_of_medical_text_and_images","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"aFRubmJTRUxuMTRaN3RjUjI3YWVQL1pieDlvNVBBeFhoRjQ3SGtzbENmKzh3QU9KcjY4OWRONmtuWkhWUnI2Zi0tV2FYc2FXWE9lSVRDb2FLNVRsTGlJZz09--3059378b032a6c1401058e34f35cc946f6c4af3e"},"attachments":[{"id":88602038,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602038/thumbnails/1.jpg","file_name":"D2_899_MEDINFO2013.pdf","download_url":"https://www.academia.edu/attachments/88602038/download_file","bulk_download_file_name":"Khresmoi_multilingual_semantic_search_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602038/D2_899_MEDINFO2013-libre.pdf?1657847407=\u0026response-content-disposition=attachment%3B+filename%3DKhresmoi_multilingual_semantic_search_of.pdf\u0026Expires=1740038824\u0026Signature=DcIDN1~GRp7czvQXzn141zT5Y9Ewy1lHWAxfKsjs33ZjqomXmlQEQ3mordnxN18-07IAqCSKfKu11U~0~U3OftwHwl7bXUGsOICmaDl~eI7DZOvcPd4fa0Wn~nZpI7GE5crX-XgcFZ6WGCUKGfEs7wRN7D~2OyMmeASQAvFGyigdw11DjsLqXakUPZdGfj-iffTTrwtYoD13oYscV631nbDROVlq0MyhFVjMdXELuIEtMwOs7NUJQ54EWr-SK9w0bOYcQeILrfFc2KxnN-VRM0l-SuC8w-Bt74T8hXsWhL2XJoX88ytjZna4f9OUMQFKH8KgpLydFio8Ubv6V02R6Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157824"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157824/Using_argumentation_to_extract_key_sentences_from_biomedical_abstracts"><img alt="Research paper thumbnail of Using argumentation to extract key sentences from biomedical abstracts" class="work-thumbnail" src="https://attachments.academia-assets.com/88602037/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157824/Using_argumentation_to_extract_key_sentences_from_biomedical_abstracts">Using argumentation to extract key sentences from biomedical abstracts</a></div><div class="wp-workCard_item"><span>International Journal of Medical Informatics</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Knowledge bases support multiple research e orts such as providing contextual information for bio...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Knowledge bases support multiple research e orts such as providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. Some knowledge bases are automatically constructed, but most are populated via some form of manual curation. Manual curation is time consuming and di cult to scale in the context of an increasing publication rate. A recently described "data programming" paradigm seeks to circumvent this arduous process by combining distant supervision with simple rules and heuristics written as labeling functions that can be automatically applied to inputs. Unfortunately writing useful label functions requires substantial error analysis and is a nontrivial task: in early e orts to use data programming we found that producing each label function could take a few days. Producing a biomedical knowledge base with multiple node and edge types could take hundreds or possibly thousands of label functions. In this paper we sought to evaluate the extent to which label functions could be re-used across edge types. We used a subset of Hetionet v1 that centered on disease, compound, and gene nodes to evaluate this approach. We compared a baseline distant supervision model with the same distant supervision resources added to edge-type-speci c label functions, edgetype-mismatch label functions, and all label functions. We con rmed that adding additional edge-typespeci c label functions improves performance. We also found that adding one or a few edge-typemismatch label functions nearly always improved performance. Adding a large number of edge-typemismatch label functions produce variable performance that depends on the edge type being predicted and the label function's edge type source. Lastly, we show that this approach, even on this subgraph of Hetionet, could add new edges to Hetionet v1 with high con dence. We expect that practical use of this strategy would include additional ltering and scoring methods which would further enhance precision. .</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e7ac100d8052a2a9daffef853c3dfcc8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602037,"asset_id":83157824,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602037/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157824"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157824"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157824; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157824]").text(description); $(".js-view-count[data-work-id=83157824]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157824; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157824']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e7ac100d8052a2a9daffef853c3dfcc8" } } $('.js-work-strip[data-work-id=83157824]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157824,"title":"Using argumentation to extract key sentences from biomedical abstracts","internal_url":"https://www.academia.edu/83157824/Using_argumentation_to_extract_key_sentences_from_biomedical_abstracts","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill"},"attachments":[{"id":88602037,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602037/thumbnails/1.jpg","file_name":"730085.full.pdf","download_url":"https://www.academia.edu/attachments/88602037/download_file","bulk_download_file_name":"Using_argumentation_to_extract_key_sente.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602037/730085.full-libre.pdf?1657847409=\u0026response-content-disposition=attachment%3B+filename%3DUsing_argumentation_to_extract_key_sente.pdf\u0026Expires=1739960627\u0026Signature=duFEZrh~qRYs2dEIaA6a~A-lcoDRcDaG2dy2GKSZoLYR4FwcYqtsmr55OzDhQqcpnVLLXOJFhU67zO14SeatPuU0rDIqNUjR2fSWHAKiRcS8Iywadb13Y3V62m7zoysJGBCsunGYdCUbftmB9n2d~CcofH7JhnoCLKPVM6R-9ROWgqqThV3rjKGxrW7N2I9r0mWC9t8NlrO4OR~Sy~jDwnSXEM9JFV3kXEmkQnngU3TOGMbHYBN3QCTNvzurmFReMXiXzW05Wgsz4AG4vgL0ktvFcQ-11p8ECS2TShE9es8K6bHtkLZnqf17jOeQYXMwaRvCa8EzJNH51Jdl~FdcWw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83157798"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83157798/Managing_the_data_deluge_data_driven"><img alt="Research paper thumbnail of Managing the data deluge: data-driven" class="work-thumbnail" src="https://attachments.academia-assets.com/88602008/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83157798/Managing_the_data_deluge_data_driven">Managing the data deluge: data-driven</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The available curated data lag behind current biological knowledge contained in the literature. T...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The available curated data lag behind current biological knowledge contained in the literature. Text mining can assist biologists and curators to locate and access this knowledge, for instance by characterizing the functional profile of publications. Gene Ontology (GO) category assignment in free text already supports various applications, such as powering ontology-based search engines, finding curation-relevant articles (triage) or helping the curator to identify and encode functions. Popular text mining tools for GO classification are based on so called thesaurus-based-or dictionary-basedapproaches, which exploit similarities between the input text and GO terms themselves. But their effectiveness remains limited owing to the complex nature of GO terms, which rarely occur in text. In contrast, machine learning approaches exploit similarities between the input text and already curated instances contained in a knowledge base to infer a functional profile. GO Annotations (GOA) and MEDLINE make possible to exploit a growing amount of curated abstracts (97 000 in November 2012) for populating this knowledge base. Our study compares a state-of-the-art thesaurus-based system with a machine learning system (based on a k-Nearest Neighbours algorithm) for the task of proposing a functional profile for unseen MEDLINE abstracts, and shows how resources and performances have evolved. Systems are evaluated on their ability to propose for a given abstract the GO terms (2.8 on average) used for curation in GOA. We show that since 2006, although a massive effort was put into adding synonyms in GO (+300%), our thesaurus-based system effectiveness is rather constant, reaching from 0.28 to 0.31 for Recall at 20 (R20). In contrast, thanks to its knowledge base growth, our machine learning system has steadily improved, reaching from 0.38 in 2006 to 0.56 for R20 in 2012. Integrated in semi-automatic workflows or in fully automatic pipelines, such systems are more and more efficient to provide assistance to biologists.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="64666b96c02a7c2f083cbf395deef86f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88602008,"asset_id":83157798,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88602008/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83157798"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83157798"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83157798; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83157798]").text(description); $(".js-view-count[data-work-id=83157798]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83157798; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83157798']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "64666b96c02a7c2f083cbf395deef86f" } } $('.js-work-strip[data-work-id=83157798]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83157798,"title":"Managing the data deluge: data-driven","internal_url":"https://www.academia.edu/83157798/Managing_the_data_deluge_data_driven","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill"},"attachments":[{"id":88602008,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88602008/thumbnails/1.jpg","file_name":"download.pdf","download_url":"https://www.academia.edu/attachments/88602008/download_file","bulk_download_file_name":"Managing_the_data_deluge_data_driven.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88602008/download-libre.pdf?1657847392=\u0026response-content-disposition=attachment%3B+filename%3DManaging_the_data_deluge_data_driven.pdf\u0026Expires=1739960627\u0026Signature=N0~vStN6b1mW2NdfLbaJc35S7qg1jwaUttycUl5712SQAbDUQJcKg7PNWuzlhAozO66~S7IyJIWiwXdS5izaFqXULCWlgWEKTMduZGh0m56IuyxH4w95-6yqM5AzI6T0FTc3Z9E-uXcdtp2F1Xb~Y7YPhhdPmvkutrNJ-xyjm9ZcoW05KSZYO46T4bVIvT2Aync9abqbgz8Af3gVh3fwXLhZ0aqCd5-oiOQMzNMBy7zCwI6ODx22wkhvlg5dxXuuoM2kIERqm4M2y4xZkzBAWMJUdKnInKN78oDuSpmnlyc7SBQajwChits0l7eDXykNlkRle5dzrNXLwS8zOBvRlQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78975880"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78975880/Variomes_a_high_recall_search_engine_to_support_the_curation_of_genomic_variants"><img alt="Research paper thumbnail of Variomes: a high recall search engine to support the curation of genomic variants" class="work-thumbnail" src="https://attachments.academia-assets.com/85852696/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78975880/Variomes_a_high_recall_search_engine_to_support_the_curation_of_genomic_variants">Variomes: a high recall search engine to support the curation of genomic variants</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Precision oncology relies on the use of treatments targeting specific genetic variants. However, ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Precision oncology relies on the use of treatments targeting specific genetic variants. However, identifying clinically actionable variants as well as relevant information likely to be used to treat a patient with a given cancer is a labor-intensive task, which includes searching the literature for a large set of variants. The lack of universally adopted standard nomenclature for variants requires the development of variant-specific literature search engines. We develop a system to perform triage of publications relevant to support an evidence-based decision. Together with providing a ranked list of articles for a given variant, the system is also able to prioritize variants, as found in a Variant Calling Format, assuming that the clinical actionability of a genetic variant is correlated with the volume of literature published about the variant. Our system searches within three pre-annotated document collections: MEDLINE abstracts, PubMed Central full-text articles and ClinicalTrial...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2b228e6930b2761949613232d1ac6997" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85852696,"asset_id":78975880,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85852696/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78975880"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78975880"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78975880; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78975880]").text(description); $(".js-view-count[data-work-id=78975880]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78975880; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78975880']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2b228e6930b2761949613232d1ac6997" } } $('.js-work-strip[data-work-id=78975880]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78975880,"title":"Variomes: a high recall search engine to support the curation of genomic variants","internal_url":"https://www.academia.edu/78975880/Variomes_a_high_recall_search_engine_to_support_the_curation_of_genomic_variants","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"ZlNhM2ZvcjcvMnRPODJ2Qm5qQWthMlljM2szNDMwQk5SVzdXUXZ2RVdQdVo5OHhVQlJGWnhCSDNIakp6eEQ0YS0tWUZzMmRsS0REZHc0YVFsYkNWTHd5dz09--2f44b04b5b090c660889e962455ed5d08d9dfafd"},"attachments":[{"id":85852696,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85852696/thumbnails/1.jpg","file_name":"2021.05.29.446224.full.pdf","download_url":"https://www.academia.edu/attachments/85852696/download_file","bulk_download_file_name":"Variomes_a_high_recall_search_engine_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85852696/2021.05.29.446224.full-libre.pdf?1652296553=\u0026response-content-disposition=attachment%3B+filename%3DVariomes_a_high_recall_search_engine_to.pdf\u0026Expires=1740038824\u0026Signature=E54rqfstkrsljzCJWwgbmZ73lpuySEauF7Eb-OPHrAaurx7bax2wHnWukjltMPqYES-aLLf6tnHlVS1kTTJK20VsS3cuUWPtSJnV8BicTGf8glprGsJw5Sr-P6XBYPkiu7WEtUpWMsTzfYtqUaqQWd0pb6mEmUoiYvgoa-soTUWeNQOOm0Z8-51TP~stSKOabY-VXIoenqeDYOaRNrtoxeLIZUz6rEB2WJDBLA9XPnIraozdGVsEI5Bv0piGHAJluHOsqbGpuli8xPZpK49bGy4rjMZZbM8TqCCgFlSy4m6ffS130ye0j2-JGPuQkAStKRO8NLmbdFOtaRjeou4tBQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="65163609"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/65163609/Rapport_de_fin_de_projet_de_lACI_Ecole_et_Sciences_Cognitives_Mod%C3%A9lisation_cognitive_d%C3%A9l%C3%A8ves_en_alg%C3%A8bre_et_construction_de_strat%C3%A9gies_denseignement_dans_un_contexte_technologique"><img alt="Research paper thumbnail of Rapport de fin de projet de l'ACI: Ecole et Sciences Cognitives-Modélisation cognitive d'élèves en algèbre et construction de stratégies d'enseignement dans un contexte technologique" class="work-thumbnail" src="https://attachments.academia-assets.com/76878977/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/65163609/Rapport_de_fin_de_projet_de_lACI_Ecole_et_Sciences_Cognitives_Mod%C3%A9lisation_cognitive_d%C3%A9l%C3%A8ves_en_alg%C3%A8bre_et_construction_de_strat%C3%A9gies_denseignement_dans_un_contexte_technologique">Rapport de fin de projet de l'ACI: Ecole et Sciences Cognitives-Modélisation cognitive d'élèves en algèbre et construction de stratégies d'enseignement dans un contexte technologique</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Modélisation cognitive d'élèves en algèbre et construction de stratégies d'enseignement dans un c...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Modélisation cognitive d'élèves en algèbre et construction de stratégies d'enseignement dans un contexte technologique</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2c644ce5bfddf3625bfd142b63d25ec4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":76878977,"asset_id":65163609,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/76878977/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="65163609"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="65163609"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 65163609; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=65163609]").text(description); $(".js-view-count[data-work-id=65163609]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 65163609; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='65163609']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2c644ce5bfddf3625bfd142b63d25ec4" } } $('.js-work-strip[data-work-id=65163609]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":65163609,"title":"Rapport de fin de projet de l'ACI: Ecole et Sciences Cognitives-Modélisation cognitive d'élèves en algèbre et construction de stratégies d'enseignement dans un contexte technologique","internal_url":"https://www.academia.edu/65163609/Rapport_de_fin_de_projet_de_lACI_Ecole_et_Sciences_Cognitives_Mod%C3%A9lisation_cognitive_d%C3%A9l%C3%A8ves_en_alg%C3%A8bre_et_construction_de_strat%C3%A9gies_denseignement_dans_un_contexte_technologique","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"ekZOVDdPNUFpb2lkdUE2L25BUDJBNS9Od213K2srZUIrNnBNOStPdFlEQ1dMR0pkbTVUdGJZZCswY2pmTG9hRi0tNXQ0MHR6dWdoUU8rUitIZjZBSzJxUT09--89a6266942acc9a3cb2c5e535d070c1aa90e03dd"},"attachments":[{"id":76878977,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/76878977/thumbnails/1.jpg","file_name":"CLLeib123.pdf","download_url":"https://www.academia.edu/attachments/76878977/download_file","bulk_download_file_name":"Rapport_de_fin_de_projet_de_lACI_Ecole_e.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/76878977/CLLeib123-libre.pdf?1639991206=\u0026response-content-disposition=attachment%3B+filename%3DRapport_de_fin_de_projet_de_lACI_Ecole_e.pdf\u0026Expires=1740038824\u0026Signature=A4aodA2IcOt0JiR8M8URctrsALwVk6SvgtkOZ2JXJcEvw9mOL7ro-y4M4rqEIrrhNk0KLF2q6pErgXTNMX19QJfEgmyG5IKZeEsNN3mUyCeYQ5iHr9mRg3dA0BbWMc0YE5AOhAL3yIaHYzGwp5CaqKTsH64CUCTk32Uu9Ax0PfEs5O-6W0GbJl1ntSKd5XGN2u7s7uxwHditqBHatLpmoJdN1bGVwdwcLjs4GfsUjSqAcYVvudy0Ux7s6AtQjYeANIpa6mhtnnnD651zt42nKNCPcP26eRi9heb~Mua7ITl2jRb5LeVxpAkH1Uykg6rkN4A8tS0ROwSJ0AvqAWOJoA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="55325216"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/55325216/SIB_Literature_Services_RESTful_customizable_search_engines_in_biomedical_literature_enriched_with_automatically_mapped_biomedical_concepts"><img alt="Research paper thumbnail of SIB Literature Services: RESTful customizable search engines in biomedical literature, enriched with automatically mapped biomedical concepts" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/55325216/SIB_Literature_Services_RESTful_customizable_search_engines_in_biomedical_literature_enriched_with_automatically_mapped_biomedical_concepts">SIB Literature Services: RESTful customizable search engines in biomedical literature, enriched with automatically mapped biomedical concepts</a></div><div class="wp-workCard_item"><span>Nucleic Acids Research</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thanks to recent efforts by the text mining community, biocurators have now access to plenty of g...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thanks to recent efforts by the text mining community, biocurators have now access to plenty of good tools and Web interfaces for identifying and visualizing biomedical entities in literature. Yet, many of these systems start with a PubMed query, which is limited by strong Boolean constraints. Some semantic search engines exploit entities for Information Retrieval, and/or deliver relevance-based ranked results. Yet, they are not designed for supporting a specific curation workflow, and allow very limited control on the search process. The Swiss Institute of Bioinformatics Literature Services (SIBiLS) provide personalized Information Retrieval in the biological literature. Indeed, SIBiLS allow fully customizable search in semantically enriched contents, based on keywords and/or mapped biomedical entities from a growing set of standardized and legacy vocabularies. The services have been used and favourably evaluated to assist the curation of genes and gene products, by delivering cust...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="55325216"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="55325216"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 55325216; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=55325216]").text(description); $(".js-view-count[data-work-id=55325216]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 55325216; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='55325216']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=55325216]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":55325216,"title":"SIB Literature Services: RESTful customizable search engines in biomedical literature, enriched with automatically mapped biomedical concepts","internal_url":"https://www.academia.edu/55325216/SIB_Literature_Services_RESTful_customizable_search_engines_in_biomedical_literature_enriched_with_automatically_mapped_biomedical_concepts","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"Sy9SVGVVR1M1cVY4NHpyd2hOQkxuQXU3UndnVmo1RTFaY0c2OEVDaHQxNFhzZnRiUkRwT0VkdnA0cHZvSTR3dC0tZzNaa2ViZmxFVVJOYU8wNTU3WWRBZz09--f5aadaedaafe56aa95422e0958bf0452baff9958"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="55325215"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/55325215/Triage_by_ranking_to_support_the_curation_of_protein_interactions"><img alt="Research paper thumbnail of Triage by ranking to support the curation of protein interactions" class="work-thumbnail" src="https://attachments.academia-assets.com/71248158/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/55325215/Triage_by_ranking_to_support_the_curation_of_protein_interactions">Triage by ranking to support the curation of protein interactions</a></div><div class="wp-workCard_item"><span>Database</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Today, molecular biology databases are the cornerstone of knowledge sharing for life and health s...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Today, molecular biology databases are the cornerstone of knowledge sharing for life and health sciences. The curation and maintenance of these resources are labour intensive. Although text mining is gaining impetus among curators, its integration in curation workflow has not yet been widely adopted. The Swiss Institute of Bioinformatics Text Mining and CALIPHO groups joined forces to design a new curation support system named nextA5. In this report, we explore the integration of novel triage services to support the curation of two types of biological data: protein-protein interactions (PPIs) and posttranslational modifications (PTMs). The recognition of PPIs and PTMs poses a special challenge, as it not only requires the identification of biological entities (proteins or residues), but also that of particular relationships (e.g. binding or position). These relationships cannot be described with onto-terminological descriptors such as the Gene Ontology for molecular functions, which makes the triage task more challenging. Prioritizing papers for these tasks thus requires the development of different approaches. In this report, we propose a new method to prioritize articles containing information specific to PPIs and PTMs. The new resources (RESTful APIs, semantically annotated MEDLINE library) enrich the neXtA5 platform. We tuned the article prioritization model on a set of 100 proteins previously annotated by the CALIPHO group. The effectiveness of the triage service was tested with a dataset of 200 annotated proteins. We defined two sets of descriptors to support automatic triage: the first set to enrich for papers with PPI data, and the second for PTMs. All occurrences of</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="90f919cbad32f279fb982c4e1655a7bf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":71248158,"asset_id":55325215,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/71248158/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="55325215"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="55325215"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 55325215; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=55325215]").text(description); $(".js-view-count[data-work-id=55325215]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 55325215; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='55325215']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "90f919cbad32f279fb982c4e1655a7bf" } } $('.js-work-strip[data-work-id=55325215]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":55325215,"title":"Triage by ranking to support the curation of protein interactions","internal_url":"https://www.academia.edu/55325215/Triage_by_ranking_to_support_the_curation_of_protein_interactions","owner_id":32463478,"coauthors_can_edit":true,"owner":{"id":32463478,"first_name":"Julien","middle_initials":null,"last_name":"Gobeill","page_name":"JulienGobeill","domain_name":"hes-so","created_at":"2015-06-23T06:49:13.313-07:00","display_name":"Julien Gobeill","url":"https://hes-so.academia.edu/JulienGobeill","email":"QXJLUVVEQ3pMR1dtdWpLQnV0ekRvUm53OTQwK0hES2lZRFNDS2o2WTFLM3dCUXBGWlpPcWZHWlVnMTljUG5uQy0tTllPZ3JXcUVKeUlTTTgxaXhsUFdDZz09--08abf84a6e1f2de3055cea4319c4b6cb6c183549"},"attachments":[{"id":71248158,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/71248158/thumbnails/1.jpg","file_name":"d9940fc194a5a3866f625af3450ef1fd76f9.pdf","download_url":"https://www.academia.edu/attachments/71248158/download_file","bulk_download_file_name":"Triage_by_ranking_to_support_the_curatio.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/71248158/d9940fc194a5a3866f625af3450ef1fd76f9-libre.pdf?1633417965=\u0026response-content-disposition=attachment%3B+filename%3DTriage_by_ranking_to_support_the_curatio.pdf\u0026Expires=1740038824\u0026Signature=OXDc8jKfN8W14X-GBuOUbC37eeS827izK4HUBOA2r4Xt7AcKn5G61AW9wcqxF40A~7cc1FL~pFPBZXT0EG~VIfS2BNbk6pImwjD0E2gwhttFfZ6ESJ95-peOAm63KjM-Px6xkzpR8Jag6-g221Rmd8hfknccrr2hEQZAAR3sRR5DSmtFdAvWymnXqQhep1JBebHJR2n2sj3gDC04WEcWVX7ubrHKKqkmUfbIQbM22TEldRaFBPMjcV6k6Y5g~87oqwH-LBFc9FjZ8pKvtduPsNIEzQodl~h9-~z5ig0I~9bHaP5kUsR4uUe9KpcZaU650AOZgZK5RePaeoj6Jsb4CQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/google_contacts-0dfb882d836b94dbcb4a2d123d6933fc9533eda5be911641f20b4eb428429600.js"], function() { // from javascript_helper.rb $('.js-google-connect-button').click(function(e) { e.preventDefault(); GoogleContacts.authorize_and_show_contacts(); Aedu.Dismissibles.recordClickthrough("WowProfileImportContactsPrompt"); }); $('.js-update-biography-button').click(function(e) { e.preventDefault(); Aedu.Dismissibles.recordClickthrough("UpdateUserBiographyPrompt"); $.ajax({ url: $r.api_v0_profiles_update_about_path({ subdomain_param: 'api', about: "", }), type: 'PUT', success: function(response) { location.reload(); } }); }); $('.js-work-creator-button').click(function (e) { e.preventDefault(); window.location = $r.upload_funnel_document_path({ source: encodeURIComponent(""), }); }); $('.js-video-upload-button').click(function (e) { e.preventDefault(); window.location = $r.upload_funnel_video_path({ source: encodeURIComponent(""), }); }); $('.js-do-this-later-button').click(function() { $(this).closest('.js-profile-nag-panel').remove(); Aedu.Dismissibles.recordDismissal("WowProfileImportContactsPrompt"); }); $('.js-update-biography-do-this-later-button').click(function(){ $(this).closest('.js-profile-nag-panel').remove(); Aedu.Dismissibles.recordDismissal("UpdateUserBiographyPrompt"); }); $('.wow-profile-mentions-upsell--close').click(function(){ $('.wow-profile-mentions-upsell--panel').hide(); Aedu.Dismissibles.recordDismissal("WowProfileMentionsUpsell"); }); $('.wow-profile-mentions-upsell--button').click(function(){ Aedu.Dismissibles.recordClickthrough("WowProfileMentionsUpsell"); }); new WowProfile.SocialRedesignUserWorks({ initialWorksOffset: 20, allWorksOffset: 20, maxSections: 1 }) }); </script> </div></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile_edit-5ea339ee107c863779f560dd7275595239fed73f1a13d279d2b599a28c0ecd33.js","https://a.academia-assets.com/assets/add_coauthor-22174b608f9cb871d03443cafa7feac496fb50d7df2d66a53f5ee3c04ba67f53.js","https://a.academia-assets.com/assets/tab-dcac0130902f0cc2d8cb403714dd47454f11fc6fb0e99ae6a0827b06613abc20.js","https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js"], function() { // from javascript_helper.rb window.ae = window.ae || {}; window.ae.WowProfile = window.ae.WowProfile || {}; if(Aedu.User.current && Aedu.User.current.id === $viewedUser.id) { window.ae.WowProfile.current_user_edit = {}; new WowProfileEdit.EditUploadView({ el: '.js-edit-upload-button-wrapper', model: window.$current_user, }); new AddCoauthor.AddCoauthorsController(); } var userInfoView = new WowProfile.SocialRedesignUserInfo({ recaptcha_key: "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB" }); WowProfile.router = new WowProfile.Router({ userInfoView: userInfoView }); Backbone.history.start({ pushState: true, root: "/" + $viewedUser.page_name }); new WowProfile.UserWorksNav() }); </script> </div> <div class="bootstrap login"><div class="modal fade login-modal" id="login-modal"><div class="login-modal-dialog modal-dialog"><div class="modal-content"><div class="modal-header"><button class="close close" data-dismiss="modal" type="button"><span aria-hidden="true">×</span><span class="sr-only">Close</span></button><h4 class="modal-title text-center"><strong>Log In</strong></h4></div><div class="modal-body"><div class="row"><div class="col-xs-10 col-xs-offset-1"><button class="btn btn-fb btn-lg btn-block btn-v-center-content" id="login-facebook-oauth-button"><svg style="float: left; width: 19px; line-height: 1em; margin-right: .3em;" aria-hidden="true" focusable="false" data-prefix="fab" data-icon="facebook-square" class="svg-inline--fa fa-facebook-square fa-w-14" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path fill="currentColor" d="M400 32H48A48 48 0 0 0 0 80v352a48 48 0 0 0 48 48h137.25V327.69h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.27c-30.81 0-40.42 19.12-40.42 38.73V256h68.78l-11 71.69h-57.78V480H400a48 48 0 0 0 48-48V80a48 48 0 0 0-48-48z"></path></svg><small><strong>Log in</strong> with <strong>Facebook</strong></small></button><br /><button class="btn btn-google btn-lg btn-block btn-v-center-content" id="login-google-oauth-button"><svg style="float: left; width: 22px; line-height: 1em; margin-right: .3em;" aria-hidden="true" focusable="false" data-prefix="fab" data-icon="google-plus" class="svg-inline--fa fa-google-plus fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M256,8C119.1,8,8,119.1,8,256S119.1,504,256,504,504,392.9,504,256,392.9,8,256,8ZM185.3,380a124,124,0,0,1,0-248c31.3,0,60.1,11,83,32.3l-33.6,32.6c-13.2-12.9-31.3-19.1-49.4-19.1-42.9,0-77.2,35.5-77.2,78.1S142.3,334,185.3,334c32.6,0,64.9-19.1,70.1-53.3H185.3V238.1H302.2a109.2,109.2,0,0,1,1.9,20.7c0,70.8-47.5,121.2-118.8,121.2ZM415.5,273.8v35.5H380V273.8H344.5V238.3H380V202.8h35.5v35.5h35.2v35.5Z"></path></svg><small><strong>Log in</strong> with <strong>Google</strong></small></button><br /><style type="text/css">.sign-in-with-apple-button { width: 100%; height: 52px; border-radius: 3px; border: 1px solid black; cursor: pointer; } .sign-in-with-apple-button > div { margin: 0 auto; / This centers the Apple-rendered button horizontally }</style><script src="https://appleid.cdn-apple.com/appleauth/static/jsapi/appleid/1/en_US/appleid.auth.js" type="text/javascript"></script><div class="sign-in-with-apple-button" data-border="false" data-color="white" id="appleid-signin"><span   ="Sign Up with Apple" class="u-fs11"></span></div><script>AppleID.auth.init({ clientId: 'edu.academia.applesignon', scope: 'name email', redirectURI: 'https://www.academia.edu/sessions', state: "de75db6c5b5cd67d29ab9420640de265179e3817c5a5153cafb17d5ab4ced201", });</script><script>// Hacky way of checking if on fast loswp if (window.loswp == null) { (function() { const Google = window?.Aedu?.Auth?.OauthButton?.Login?.Google; const Facebook = window?.Aedu?.Auth?.OauthButton?.Login?.Facebook; if (Google) { new Google({ el: '#login-google-oauth-button', rememberMeCheckboxId: 'remember_me', track: null }); } if (Facebook) { new Facebook({ el: '#login-facebook-oauth-button', rememberMeCheckboxId: 'remember_me', track: null }); } })(); }</script></div></div></div><div class="modal-body"><div class="row"><div class="col-xs-10 col-xs-offset-1"><div class="hr-heading login-hr-heading"><span class="hr-heading-text">or</span></div></div></div></div><div class="modal-body"><div class="row"><div class="col-xs-10 col-xs-offset-1"><form class="js-login-form" action="https://www.academia.edu/sessions" accept-charset="UTF-8" method="post"><input type="hidden" name="authenticity_token" value="Kwa6wyXfBbYU6lS8FqXB_hViMbICZxShl0Z_5-tpDm2EhBa3IV-UQCsQG1PkMm6fhwqMZdobu2zV9nO5fCettw" autocomplete="off" /><div class="form-group"><label class="control-label" for="login-modal-email-input" style="font-size: 14px;">Email</label><input class="form-control" id="login-modal-email-input" name="login" type="email" /></div><div class="form-group"><label class="control-label" for="login-modal-password-input" style="font-size: 14px;">Password</label><input class="form-control" id="login-modal-password-input" name="password" type="password" /></div><input type="hidden" name="post_login_redirect_url" id="post_login_redirect_url" value="https://hes-so.academia.edu/JulienGobeill" autocomplete="off" /><div class="checkbox"><label><input type="checkbox" name="remember_me" id="remember_me" value="1" checked="checked" /><small style="font-size: 12px; margin-top: 2px; display: inline-block;">Remember me on this computer</small></label></div><br><input type="submit" name="commit" value="Log In" class="btn btn-primary btn-block btn-lg js-login-submit" data-disable-with="Log In" /></br></form><script>typeof window?.Aedu?.recaptchaManagedForm === 'function' && window.Aedu.recaptchaManagedForm( document.querySelector('.js-login-form'), document.querySelector('.js-login-submit') );</script><small style="font-size: 12px;"><br />or <a data-target="#login-modal-reset-password-container" data-toggle="collapse" href="javascript:void(0)">reset password</a></small><div class="collapse" id="login-modal-reset-password-container"><br /><div class="well margin-0x"><form class="js-password-reset-form" action="https://www.academia.edu/reset_password" accept-charset="UTF-8" method="post"><input type="hidden" name="authenticity_token" value="Gliv_whEbyYFooij9nKnmsXAZjped9BZIiPuS5XyzbK12gOLDMT-0DpYx0wE5Qj7V6jb7YYLf5Rgk-IVArxuaA" autocomplete="off" /><p>Enter the email address you signed up with and we'll email you a reset link.</p><div class="form-group"><input class="form-control" name="email" type="email" /></div><script src="https://recaptcha.net/recaptcha/api.js" async defer></script> <script> var invisibleRecaptchaSubmit = function () { var closestForm = function (ele) { var curEle = ele.parentNode; while (curEle.nodeName !== 'FORM' && curEle.nodeName !== 'BODY'){ curEle = curEle.parentNode; } return curEle.nodeName === 'FORM' ? curEle : null }; var eles = document.getElementsByClassName('g-recaptcha'); if (eles.length > 0) { var form = closestForm(eles[0]); if (form) { form.submit(); } } }; </script> <input type="submit" data-sitekey="6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj" data-callback="invisibleRecaptchaSubmit" class="g-recaptcha btn btn-primary btn-block" value="Email me a link" value=""/> </form></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/collapse-45805421cf446ca5adf7aaa1935b08a3a8d1d9a6cc5d91a62a2a3a00b20b3e6a.js"], function() { // from javascript_helper.rb $("#login-modal-reset-password-container").on("shown.bs.collapse", function() { $(this).find("input[type=email]").focus(); }); }); </script> </div></div></div><div class="modal-footer"><div class="text-center"><small style="font-size: 12px;">Need an account? <a rel="nofollow" href="https://www.academia.edu/signup">Click here to sign up</a></small></div></div></div></div></div></div><script>// If we are on subdomain or non-bootstrapped page, redirect to login page instead of showing modal (function(){ if (typeof $ === 'undefined') return; var host = window.location.hostname; if ((host === $domain || host === "www."+$domain) && (typeof $().modal === 'function')) { $("#nav_log_in").click(function(e) { // Don't follow the link and open the modal e.preventDefault(); $("#login-modal").on('shown.bs.modal', function() { $(this).find("#login-modal-email-input").focus() }).modal('show'); }); } })()</script> <div class="bootstrap" id="footer"><div class="footer-content clearfix text-center padding-top-7x" style="width:100%;"><ul class="footer-links-secondary footer-links-wide list-inline margin-bottom-1x"><li><a href="https://www.academia.edu/about">About</a></li><li><a href="https://www.academia.edu/press">Press</a></li><li><a href="https://www.academia.edu/documents">Papers</a></li><li><a href="https://www.academia.edu/topics">Topics</a></li><li><a href="https://www.academia.edu/journals">Academia.edu Journals</a></li><li><a rel="nofollow" href="https://www.academia.edu/hiring"><svg style="width: 13px; height: 13px;" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="briefcase" class="svg-inline--fa fa-briefcase fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M320 336c0 8.84-7.16 16-16 16h-96c-8.84 0-16-7.16-16-16v-48H0v144c0 25.6 22.4 48 48 48h416c25.6 0 48-22.4 48-48V288H320v48zm144-208h-80V80c0-25.6-22.4-48-48-48H176c-25.6 0-48 22.4-48 48v48H48c-25.6 0-48 22.4-48 48v80h512v-80c0-25.6-22.4-48-48-48zm-144 0H192V96h128v32z"></path></svg> <strong>We're Hiring!</strong></a></li><li><a rel="nofollow" href="https://support.academia.edu/hc/en-us"><svg style="width: 12px; height: 12px;" aria-hidden="true" focusable="false" data-prefix="fas" data-icon="question-circle" class="svg-inline--fa fa-question-circle fa-w-16" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M504 256c0 136.997-111.043 248-248 248S8 392.997 8 256C8 119.083 119.043 8 256 8s248 111.083 248 248zM262.655 90c-54.497 0-89.255 22.957-116.549 63.758-3.536 5.286-2.353 12.415 2.715 16.258l34.699 26.31c5.205 3.947 12.621 3.008 16.665-2.122 17.864-22.658 30.113-35.797 57.303-35.797 20.429 0 45.698 13.148 45.698 32.958 0 14.976-12.363 22.667-32.534 33.976C247.128 238.528 216 254.941 216 296v4c0 6.627 5.373 12 12 12h56c6.627 0 12-5.373 12-12v-1.333c0-28.462 83.186-29.647 83.186-106.667 0-58.002-60.165-102-116.531-102zM256 338c-25.365 0-46 20.635-46 46 0 25.364 20.635 46 46 46s46-20.636 46-46c0-25.365-20.635-46-46-46z"></path></svg> <strong>Help Center</strong></a></li></ul><ul class="footer-links-tertiary list-inline margin-bottom-1x"><li class="small">Find new research papers in:</li><li class="small"><a href="https://www.academia.edu/Documents/in/Physics">Physics</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Chemistry">Chemistry</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Biology">Biology</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Health_Sciences">Health Sciences</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Ecology">Ecology</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Earth_Sciences">Earth Sciences</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a></li><li class="small"><a href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a></li></ul></div></div><div class="DesignSystem" id="credit" style="width:100%;"><ul class="u-pl0x footer-links-legal list-inline"><li><a rel="nofollow" href="https://www.academia.edu/terms">Terms</a></li><li><a rel="nofollow" href="https://www.academia.edu/privacy">Privacy</a></li><li><a rel="nofollow" href="https://www.academia.edu/copyright">Copyright</a></li><li>Academia ©2025</li></ul></div><script> //<![CDATA[ window.detect_gmtoffset = true; window.Academia && window.Academia.set_gmtoffset && Academia.set_gmtoffset('/gmtoffset'); //]]> </script> <div id='overlay_background'></div> <div id='bootstrap-modal-container' class='bootstrap'></div> <div id='ds-modal-container' class='bootstrap DesignSystem'></div> <div id='full-screen-modal'></div> </div> </body> </html>