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(PDF) Overview of the BioCreative VI text-mining services for Kinome Curation Track
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window.loswp.work = {"work":{"id":83157865,"created_at":"2022-07-14T14:12:16.942-07:00","from_world_paper_id":210557611,"updated_at":"2024-11-23T15:48:20.864-08:00","_data":{"publisher":"Oxford University Press (OUP)","grobid_abstract":"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 The track has exploited an unpublished curated data set from the neXtProt database.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.","publication_date":"2018,,","publication_name":"Database","grobid_abstract_attachment_id":"88602057"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"low","language":"en","title":"Overview of the BioCreative VI text-mining services for Kinome Curation Track","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [32463478]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":88602057,"attachmentType":"pdf"}"><img alt="First page of “Overview of the BioCreative VI text-mining services for Kinome Curation Track”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/88602057/mini_magick20220714-11339-677b24.png?1657853651" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Overview of the BioCreative VI text-mining services for Kinome Curation Track</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="32463478" href="https://hes-so.academia.edu/JulienGobeill"><img alt="Profile image of Julien Gobeill" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Julien Gobeill</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2018, Database</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">8 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 83157865; 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if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">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.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":88602057,"attachmentType":"pdf","workUrl":"https://www.academia.edu/83157865/Overview_of_the_BioCreative_VI_text_mining_services_for_Kinome_Curation_Track"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":88602057,"attachmentType":"pdf","workUrl":"https://www.academia.edu/83157865/Overview_of_the_BioCreative_VI_text_mining_services_for_Kinome_Curation_Track"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Extracting information on kinases from biomedical literature is an important task which has direct implications for applications such as drug design. In this work, we develop KinDER, Kinase Document Extractor and Ranker, a biomedical natural language processing tool for extracting functional and disease related information on kinases. This tool combines information retrieval and machine learning techniques to automatically extract information about protein kinases. First, it uses several bio-ontologies to retrieve documents related to kinases and then uses a supervised classification model to rank them according to their relevance. This was developed to participate in the Text-mining services for Human Kinome Curation Track of the BioCreative VI challenge. According to the official BioCreative evaluation results, KinDER provides stateof-the-art performance for extracting functional information on kinases from abstracts. Keywords—k...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"KinDER : A Biocuration Tool for Extracting Kinase Knowledge from Biomedical Literature","attachmentId":81311074,"attachmentType":"pdf","work_url":"https://www.academia.edu/72350624/KinDER_A_Biocuration_Tool_for_Extracting_Kinase_Knowledge_from_Biomedical_Literature","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/72350624/KinDER_A_Biocuration_Tool_for_Extracting_Kinase_Knowledge_from_Biomedical_Literature"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="13109389" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/13109389/Getting_to_the_c_ore_of_knowledge_mining_biomedical_literature">Getting to the (c)ore of knowledge: mining biomedical literature</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32360697" href="https://independent.academia.edu/BerryBruijn">Berry de Bruijn</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Medical Informatics, 2002</p><p class="ds-related-work--abstract ds2-5-body-sm">Literature mining is the process of extracting and combining facts from scientific publications. In recent years, many computer programs have been designed to extract various molecular biology findings from Medline abstracts or full text articles. The present article describes the range of text mining techniques that have been applied to scientific documents. It divides 'automated reading' into four general subtasks: text categorization, named entity tagging, fact extraction, and collection-wide analysis. Literature mining offers powerful methods to support knowledge discovery and the construction of topic maps and ontologies. An overview is given of recent developments in medical language processing. Special attention is given to the domain particularities of molecular biology, and the emerging synergy between literature mining and molecular databases accessible through Internet.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Getting to the (c)ore of knowledge: mining biomedical literature","attachmentId":45658930,"attachmentType":"pdf","work_url":"https://www.academia.edu/13109389/Getting_to_the_c_ore_of_knowledge_mining_biomedical_literature","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/13109389/Getting_to_the_c_ore_of_knowledge_mining_biomedical_literature"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="54002860" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/54002860/PESCADOR_a_web_based_tool_to_assist_text_mining_of_biointeractions_extracted_from_PubMed_queries">PESCADOR, a web-based tool to assist text-mining of biointeractions extracted from PubMed queries</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="102586561" href="https://independent.academia.edu/FernandaStussi">Fernanda Stussi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">BMC Bioinformatics, 2011</p><p class="ds-related-work--abstract ds2-5-body-sm">Background: Biological function is greatly dependent on the interactions of proteins with other proteins and genes. Abstracts from the biomedical literature stored in the NCBI's PubMed database can be used for the derivation of interactions between genes and proteins by identifying the co-occurrences of their terms. Often, the amount of interactions obtained through such an approach is large and may mix processes occurring in different contexts. Current tools do not allow studying these data with a focus on concepts of relevance to a user, for example, interactions related to a disease or to a biological mechanism such as protein aggregation. Results: To help the concept-oriented exploration of such data we developed PESCADOR, a web tool that extracts a network of interactions from a set of PubMed abstracts given by a user, and allows filtering the interaction network according to user-defined concepts. We illustrate its use in exploring protein aggregation in neurodegenerative disease and in the expansion of pathways associated to colon cancer.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"PESCADOR, a web-based tool to assist text-mining of biointeractions extracted from PubMed queries","attachmentId":70576816,"attachmentType":"pdf","work_url":"https://www.academia.edu/54002860/PESCADOR_a_web_based_tool_to_assist_text_mining_of_biointeractions_extracted_from_PubMed_queries","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/54002860/PESCADOR_a_web_based_tool_to_assist_text_mining_of_biointeractions_extracted_from_PubMed_queries"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="69883546" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/69883546/PubMedPortable_A_Framework_for_Supporting_the_Development_of_Text_Mining_Applications">PubMedPortable: A Framework for Supporting the Development of Text Mining Applications</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="46073171" href="https://unilim.academia.edu/PhilippeThomas">Philippe Thomas</a></div><p class="ds-related-work--metadata ds2-5-body-xs">PLOS ONE, 2016</p><p class="ds-related-work--abstract ds2-5-body-sm">Information extraction from biomedical literature is continuously growing in scope and importance. Many tools exist that perform named entity recognition, e.g. of proteins, chemical compounds, and diseases. Furthermore, several approaches deal with the extraction of relations between identified entities. The BioCreative community supports these developments with yearly open challenges, which led to a standardised XML text annotation format called BioC. PubMed provides access to the largest open biomedical literature repository, but there is no unified way of connecting its data to natural language processing tools. Therefore, an appropriate data environment is needed as a basis to combine different software solutions and to develop customised text mining applications. PubMedPortable builds a relational database and a full text index on PubMed citations. It can be applied either to the complete PubMed data set or an arbitrary subset of downloaded PubMed XML files. The software provides the infrastructure to combine stand-alone applications by exporting different data formats, e.g. BioC. The presented workflows show how to use PubMedPortable to retrieve, store, and analyse a disease-specific data set. The provided use cases are well documented in the PubMedPortable wiki. The open-source software library is small, easy to use, and scalable to the user's system requirements. It is freely available for Linux on the web at https://github.com/KerstenDoering/PubMedPortable and for other operating systems as a virtual container. The approach was tested extensively and applied successfully in several projects.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"PubMedPortable: A Framework for Supporting the Development of Text Mining Applications","attachmentId":79809710,"attachmentType":"pdf","work_url":"https://www.academia.edu/69883546/PubMedPortable_A_Framework_for_Supporting_the_Development_of_Text_Mining_Applications","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/69883546/PubMedPortable_A_Framework_for_Supporting_the_Development_of_Text_Mining_Applications"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="100537696" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/100537696/BNEMiner_Mining_Biomedical_Literature_for_Extraction_of_Biological_Target_Disease_and_Chemical_Entities">BNEMiner: Mining Biomedical Literature for Extraction of Biological Target, Disease and Chemical Entities</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="266671656" href="https://independent.academia.edu/SindhujaGopalan">Sindhuja Gopalan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Business Intelligence and Data Mining, 2016</p><p class="ds-related-work--abstract ds2-5-body-sm">The paper presents a novel application to extract biomedical entities automatically using machine learning techniques from large volumes of biomedical text. The data in large quantities are accumulating day by day and requires automatic extraction of information. Data mining is the science of extracting information from large data. Biomedical Named entity recognition (BioNER) is the task of data mining that extracts named entities from biological texts. In this paper, we focus on developing a BioNER system for extraction of biological target, disease and chemical entities from biomedical texts. We developed the system using graphical based machine learning technique the CRFs. We have applied a set of diverse features containing standard lexical, syntactic and orthographic features combined with novel and biologically inspired features, action terms and process verbs. The system was evaluated with three widely recognised datasets. The results demonstrated the portability and the potency of the system.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"BNEMiner: Mining Biomedical Literature for Extraction of Biological Target, Disease and Chemical Entities","attachmentId":101333669,"attachmentType":"pdf","work_url":"https://www.academia.edu/100537696/BNEMiner_Mining_Biomedical_Literature_for_Extraction_of_Biological_Target_Disease_and_Chemical_Entities","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/100537696/BNEMiner_Mining_Biomedical_Literature_for_Extraction_of_Biological_Target_Disease_and_Chemical_Entities"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="10770791" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/10770791/Benchmarking_of_the_2010_BioCreative_Challenge_III_text_mining_competition_by_the_BioGRID_and_MINT_interaction_databases">Benchmarking of the 2010 BioCreative Challenge III text-mining competition by the BioGRID and MINT interaction databases</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="26225858" href="https://scuolaiad.academia.edu/GianniCesareni">Gianni Cesareni</a></div><p class="ds-related-work--metadata ds2-5-body-xs">BMC bioinformatics, 2011</p><p class="ds-related-work--abstract ds2-5-body-sm">Background: The vast amount of data published in the primary biomedical literature represents a challenge for the automated extraction and codification of individual data elements. Biological databases that rely solely on manual extraction by expert curators are unable to comprehensively annotate the information dispersed across the entire biomedical literature. The development of efficient tools based on natural language processing (NLP) systems is essential for the selection of relevant publications, identification of data attributes and partially automated annotation. One of the tasks of the Biocreative 2010 Challenge III was devoted to the evaluation of NLP systems developed to identify articles for curation and extraction of protein-protein interaction (PPI) data. Results: The Biocreative 2010 competition addressed three tasks: gene normalization, article classification and interaction method identification. The BioGRID and MINT protein interaction databases both participated in the generation of the test publication set for gene normalization, annotated the development and test sets for article classification, and curated the test set for interaction method classification. These test datasets served as a gold standard for the evaluation of data extraction algorithms. Conclusion: The development of efficient tools for extraction of PPI data is a necessary step to achieve full curation of the biomedical literature. NLP systems can in the first instance facilitate expert curation by refining the list of candidate publications that contain PPI data; more ambitiously, NLP approaches may be able to directly extract relevant information from full-text articles for rapid inspection by expert curators. Close collaboration between biological databases and NLP systems developers will continue to facilitate the long-term objectives of both disciplines.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Benchmarking of the 2010 BioCreative Challenge III text-mining competition by the BioGRID and MINT interaction databases","attachmentId":47142517,"attachmentType":"pdf","work_url":"https://www.academia.edu/10770791/Benchmarking_of_the_2010_BioCreative_Challenge_III_text_mining_competition_by_the_BioGRID_and_MINT_interaction_databases","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/10770791/Benchmarking_of_the_2010_BioCreative_Challenge_III_text_mining_competition_by_the_BioGRID_and_MINT_interaction_databases"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="59934200" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/59934200/An_open_source_framework_for_large_scale_flexible_evaluation_of_biomedical_text_mining_systems">An open-source framework for large-scale, flexible evaluation of biomedical text mining systems</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="3430318" href="https://ucdenver.academia.edu/KevinBCohen">Kevin Bretonnel Cohen</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Biomedical Discovery and Collaboration, 2008</p><p class="ds-related-work--abstract ds2-5-body-sm">Background: Improved evaluation methodologies have been identified as a necessary prerequisite to the improvement of text mining theory and practice. This paper presents a publicly available framework that facilitates thorough, structured, and large-scale evaluations of text mining technologies. The extensibility of this framework and its ability to uncover system-wide characteristics by analyzing component parts as well as its usefulness for facilitating third-party application integration are demonstrated through examples in the biomedical domain. Results: Our evaluation framework was assembled using the Unstructured Information Management Architecture. It was used to analyze a set of gene mention identification systems involving 225 combinations of system, evaluation corpus, and correctness measure. Interactions between all three were found to affect the relative rankings of the systems. A second experiment evaluated gene normalization system performance using as input 4,097 combinations of gene mention systems and gene mention system-combining strategies. Gene mention system recall is shown to affect gene normalization system performance much more than does gene mention system precision, and high gene normalization performance is shown to be achievable with remarkably low levels of gene mention system precision. Conclusion: The software presented in this paper demonstrates the potential for novel discovery resulting from the structured evaluation of biomedical language processing systems, as well as the usefulness of such an evaluation framework for promoting collaboration between developers of biomedical language processing technologies. The code base is available as part of the BioNLP UIMA Component Repository on SourceForge.net. Background This paper investigates the hypothesis that structured evaluations are a valuable addition to the current paradigm for performance testing of large language processing systems. Support for the claim that thorough, structured evaluations are a prerequisite for further advances in the field of text mining has recently come from a surprising corner. In a recent keynote speech at the 10th annual meeting of the Conference on Natural Language Learning (CoNLL), Walter Daelemans, a noted proponent of machine-learning-based approaches to natural language processing (NLP), pointed out that the machine learning community is falling short of its potential to ask and to answer interesting and important questions not just about machine</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An open-source framework for large-scale, flexible evaluation of biomedical text mining systems","attachmentId":73606166,"attachmentType":"pdf","work_url":"https://www.academia.edu/59934200/An_open_source_framework_for_large_scale_flexible_evaluation_of_biomedical_text_mining_systems","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/59934200/An_open_source_framework_for_large_scale_flexible_evaluation_of_biomedical_text_mining_systems"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="6303407" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/6303407/A_survey_of_current_work_in_biomedical_text_mining">A survey of current work in biomedical text mining</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="9782476" href="https://independent.academia.edu/AliKo%C3%A74">Ali Koç</a></div><p class="ds-related-work--abstract ds2-5-body-sm">The volume of published biomedical research, and therefore the underlying biomedical knowledge base, is expanding at an increasing rate. Among the tools that can aid researchers in coping with this information overload are text mining and knowledge extraction. Significant progress has been made in applying text mining to named entity recognition, text classification, terminology extraction, relationship extraction and hypothesis generation. Several research groups are constructing integrated flexible text-mining systems intended for multiple uses. The major challenge of biomedical text mining over the next 5-10 years is to make these systems useful to biomedical researchers. This will require enhanced access to full text, better understanding of the feature space of biomedical literature, better methods for measuring the usefulness of systems to users, and continued cooperation with the biomedical research community to ensure that their needs are addressed.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A survey of current work in biomedical text mining","attachmentId":33144473,"attachmentType":"pdf","work_url":"https://www.academia.edu/6303407/A_survey_of_current_work_in_biomedical_text_mining","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/6303407/A_survey_of_current_work_in_biomedical_text_mining"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="104781962" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/104781962/Text_mining_approaches_in_molecular_biology_and_biomedicine">Text-mining approaches in molecular biology and biomedicine</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="70706478" href="https://independent.academia.edu/MKrallinger">Martin Krallinger</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Drug Discovery Today, 2005</p><p class="ds-related-work--abstract ds2-5-body-sm">Biomedical articles provide functional descriptions of bioentities such as chemical compounds and proteins. To extract relevant information using automatic techniques, text-mining and information-extraction approaches have been developed. These technologies have a key role in integrating biomedical information through analysis of scientific literature. In this article, important applications such as the identification of biologically relevant entities in free text and the construction of literature-based networks of protein-protein interactions will be introduced. Also, the use of text mining to aid the interpretation of microarray data and the analysis of pathology reports will be discussed. Finally, we will consider the recent evolution of this field and the efforts for community-based evaluations.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Text-mining approaches in molecular biology and biomedicine","attachmentId":104419294,"attachmentType":"pdf","work_url":"https://www.academia.edu/104781962/Text_mining_approaches_in_molecular_biology_and_biomedicine","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/104781962/Text_mining_approaches_in_molecular_biology_and_biomedicine"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="99505243" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/99505243/Towards_Pathway_Curation_Through_Literature_Mining_a_Case_Study_Using_Pharmgkb">Towards Pathway Curation Through Literature Mining – a Case Study Using Pharmgkb</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="7189477" href="https://mayoclinic.academia.edu/KavishwarWagholikar">Kavishwar Wagholikar</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Biocomputing 2014, 2013</p><p class="ds-related-work--abstract ds2-5-body-sm">The creation of biological pathway knowledge bases is largely driven by manual effort to curate based on evidences from the scientific literature. It is highly challenging for the curators to keep up with the literature. Text mining applications have been developed in the last decade to assist human curators to speed up the curation pace where majority of them aim to identify the most relevant papers for curation with little attempt to directly extract the pathway information from text. In this paper, we describe a rule-based literature mining system to extract pathway information from text. We evaluated the system using curated pharmacokinetic (PK) and pharmacodynamic (PD) pathways in PharmGKB. The system achieved an F-measure of 63.11% and 34.99% for entity extraction and event extraction respectively against all PubMed abstracts cited in PharmGKB. It may be possible to improve the system performance by incorporating using statistical machine learning approaches. This study also helped us gain insights into the barriers towards automated event extraction from text for pathway curation.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Towards Pathway Curation Through Literature Mining – a Case Study Using Pharmgkb","attachmentId":100575771,"attachmentType":"pdf","work_url":"https://www.academia.edu/99505243/Towards_Pathway_Curation_Through_Literature_Mining_a_Case_Study_Using_Pharmgkb","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/99505243/Towards_Pathway_Curation_Through_Literature_Mining_a_Case_Study_Using_Pharmgkb"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":88602057,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":88602057,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_88602057" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="3683677" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/3683677/Note_a_workbench_for_biomedical_text_mining">Note: a workbench for biomedical text mining</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="495766" href="https://uminho.academia.edu/EugenioFerreira">Eugénio C. Ferreira</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of biomedical informatics, 2009</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Note: a workbench for biomedical text mining","attachmentId":50179154,"attachmentType":"pdf","work_url":"https://www.academia.edu/3683677/Note_a_workbench_for_biomedical_text_mining","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/3683677/Note_a_workbench_for_biomedical_text_mining"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="1" data-entity-id="83586537" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/83586537/Mining_Biomedical_Publications_With_The_LAPPS_Grid">Mining Biomedical Publications With The LAPPS Grid</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="65468412" href="https://independent.academia.edu/JindongKim">Jin-dong Kim</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2018</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Mining Biomedical Publications With The LAPPS Grid","attachmentId":88881994,"attachmentType":"pdf","work_url":"https://www.academia.edu/83586537/Mining_Biomedical_Publications_With_The_LAPPS_Grid","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/83586537/Mining_Biomedical_Publications_With_The_LAPPS_Grid"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="2" data-entity-id="11145451" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/11145451/Evaluation_of_text_mining_systems_for_biology_overview_of_the_Second_BioCreative_community_challenge">Evaluation of text-mining systems for biology: overview of the Second BioCreative community challenge</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="26909016" href="https://independent.academia.edu/AlexanderMorgan4">Alexander Morgan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Genome biology, 2008</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Evaluation of text-mining systems for biology: overview of the Second BioCreative community challenge","attachmentId":46861913,"attachmentType":"pdf","work_url":"https://www.academia.edu/11145451/Evaluation_of_text_mining_systems_for_biology_overview_of_the_Second_BioCreative_community_challenge","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/11145451/Evaluation_of_text_mining_systems_for_biology_overview_of_the_Second_BioCreative_community_challenge"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="3" data-entity-id="56109616" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/56109616/Large_scale_testing_of_bibliome_informatics_for_biomedicine">Large-scale testing of bibliome informatics for biomedicine</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="43288" href="https://indiana.academia.edu/LuisRocha">Luis Rocha</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Large-scale testing of bibliome informatics for biomedicine","attachmentId":71657582,"attachmentType":"pdf","work_url":"https://www.academia.edu/56109616/Large_scale_testing_of_bibliome_informatics_for_biomedicine","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/56109616/Large_scale_testing_of_bibliome_informatics_for_biomedicine"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="4" data-entity-id="2247906" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/2247906/What_the_papers_say_text_mining_for_genomics_and_systems_biology">What the papers say: text mining for genomics and systems biology</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="393022" href="https://imperial.academia.edu/MichaelStumpf">Michael P.H. Stumpf</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Human genomics, 2010</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"What the papers say: text mining for genomics and systems biology","attachmentId":50699098,"attachmentType":"pdf","work_url":"https://www.academia.edu/2247906/What_the_papers_say_text_mining_for_genomics_and_systems_biology","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/2247906/What_the_papers_say_text_mining_for_genomics_and_systems_biology"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" 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