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PSB will bring together top researchers from North America, the Asian Pacific nations, Europe and around the world to exchange research results and address open issues in all aspects of computational biology. PSB will provide a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. PSB intends to attract a balanced combination of computer scientists and biologists, presenting significant original research, demonstrating computer systems, and facilitating formal and informal discussions on topics of importance to computational biology. To provide focus for the very broad area of biological computing, PSB is organized into a series of specific sessions. Each session will involve both formal research presentations and open discussion groups. Computational approaches to understanding the evolution of molecular function Imaging Genomics Methods to Ensure the Reproducibility of Biomedical Research Patterns in Biomedical Data - How do we find them? Precision medicine: from genotypes and molecular phenotypes towards improved health and therapies Single-cell analysis and modelling of cell population heterogeneity Papers and posters The core of the conference consists of rigorously peer-reviewed full-length papers reporting on original work. All accepted papers will be published electronically and indexed in PubMed, and the best of these will be presented orally to the entire conference. PSB will also initiate submission to PubMed Central (PMC); however, PMC indexing applies only to papers that comply with the NIH Public Access Policy. Researchers wishing to present their research without official publication are encouraged to submit a one page abstract by the abstract deadline listed below to present their work in the poster sessions. PSB 2017 Sessions: Each session has a chair who is responsible for organizing submissions. Please contact the specific session chair relevant to your interests for further information. Links on each of the session titles below lead to more detailed calls for participation for each session. Computational approaches to understanding the evolution of molecular function Imaging Genomics Methods to Ensure the Reproducibility of Biomedical Research Patterns in Biomedical Data - How do we find them? Precision medicine: from genotypes and molecular phenotypes towards improved health and therapies Single-cell analysis and modelling of cell population heterogeneity Computational approaches to understanding the evolution of molecular function Co-chairs: Yana Bromberg, Matthew Hahn, and Predrag Radivojac Our session is primarily intended to bring together researchers from the evolutionary biology and protein function communities, but we also expect other disciplines to participate. The following lists some of the areas we are interested in including in our session: Contact: Predrag Radivojac Email: predrag at indiana dot edu Imaging Genomics Co-chairs: Li Shen and Lee Cooper Imaging genomics is an emerging research field, where integrative analysis of imaging and omics data is performed to provide new insights into the phenotypic characteristics and genetic mechanisms of normal and/or disordered biological structures and functions, and to impact the development of new diagnostic, therapeutic and preventive approaches. This session aims to encourage discussion on fundamental concepts, new methods and innovative applications in this young and rapidly evolving field. Contact: Li Shen Email: shenli at iu dot edu Methods to Ensure the Reproducibility of Biomedical Research Co-chairs: Konrad J. Karczewski, Arjun K. Manrai, Chirag J. Patel, Nicholas P. Tatonetti, C. Titus Brown, John P.A. Ioannidis A few weeks ago, Vice President Joe Biden, in announcing his new research funding initiatives, criticized the current state of scientific research, which is “trapped in silos, preventing faster progress and greater reach to patients.” To make matters worse, the New England Journal of Medicine published a commentary calling scientists who repurpose data “research parasites” who may use data generated by others to demonstrate alternative hypotheses. We, the organizers of this session, believe this is absolutely unacceptable and that the concept of data hoarding not only runs contrary to the spirit of, but also actively damages scientific research. Scientific research is meant to seek objective truth, rather than promote a personal agenda, and we believe the only way to do so is through transparency and reproducibility. Contact: Konrad Karczewski Email: konradk (at) broadinstitute.org Patterns in Biomedical Data - How do we find them? Co-chairs: Anurag Verma, Anna Okula Basile, Marta Byrska-Bishop, Christian Darabos, H. Lester Kirchner, and Sarah Pendergrass Given the exponential growth of biomedical data, researchers are faced with numerous challenges in extracting and interpreting information from theses large, high-dimensional, and often noisy and incomplete data. To facilitate addressing this growing concern, this PSB 2017 session is devoted to exploring pattern recognition using data-driven approaches for biomedical and precision medicine applications. We invite manuscripts that focus on novel pattern recognition approaches, applications of established methods to heterogeneous data, and those which address the present challenges in data-driven approaches. Contact: Anna Okula Basile Email: azo121 at psu dot edu Precision medicine: from genotypes and molecular phenotypes towards improved health and therapies Co-chairs: Bruce Aronow, Steven E. Brenner, Dana C. Crawford, Sean D. Mooney, Alexander A. Morgan This session will explore new and open problems pertaining to various genome-wide and other large scale data, including rare and common SNPs, structural variants, epigenetic scans, multi-omic data, intermediate phenotypes, clinical variables from electronic medical records, disease and quantified-self sensor-based data. Contact: Dana Crawford Email: dcc64 at case dot edu Single-cell analysis and modelling of cell population heterogeneity Co-chairs: Nikolay Samusik, Sean Bendall, and Nima Aghaeepour The ability to quantify molecular events with single cell resolution is intrinsically linked to analytical advances. Until recently, many of those variations could not be systematically studied because traditional molecular biology methods, such as PCR, Western Blotting, IP, genome sequencing, microarrays and RNA-seq, lacked single cell resolution. As an exception, the field of immunology has enormously benefitted from early adoption of the single-cell analysis by flow cytometry and FACS. Flow cytometry has been pivotal to detailed characterization of various immunological processes, such as blood cell development and activating and has enabled systematic mapping of the roles of various immune cell populations in health in disease states. Cytometry has always emphasized multiparametric analysis, distinguishing various cell populations by complex combinations of multiple markers on cells. A plethora of excellent computational methods aimed at flow data has been created over the last decades. Contact: Nikolay Samusik Email: samusi at stanford dot edu </pre><div class="alert alert-warning"> Last updated by <a href="/researcher/323">Xin Yao</a> in 2016-06-17</div> </div> </div>  <div class="portlet" id="yw2"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-th"></i> Related Conferences</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw3" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw3_c0">CCF</th><th id="yw3_c1">CORE</th><th id="yw3_c2">QUALIS</th><th id="yw3_c3">Short</th><th id="yw3_c4">Full Name</th><th id="yw3_c5">Submission</th><th id="yw3_c6">Notification</th><th id="yw3_c7">Conference</th></tr> </thead> <tbody> <tr class="odd"> <td></td><td><span class="badge badge-warning">c</span></td><td><span class="badge badge-warning">b3</span></td><td>APSCC</td><td><a href="/conference/6">IEEE Asia-Pacific Services Computing 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href="/journal/53">EURASIP Journal on Information Security</a></td><td>2.500</td><td>Springer</td></tr> <tr class="even"> <td><a href="/journal/966">Interaction Studies</a></td><td>0.900</td><td>John Benjamins Publishing Company</td></tr> <tr class="odd"> <td><a href="/journal/1108">Intelligence & Robotics</a></td><td></td><td>OAE Publishing</td></tr> <tr class="even"> <td><a href="/journal/1002">International Journal of Human Capital and Information Technology Professionals</a></td><td></td><td>IGI Global Publishing</td></tr> <tr class="odd"> <td><a href="/journal/832">Journal of Digital Imaging</a></td><td>2.900</td><td>Springer</td></tr> <tr class="even"> <td><a href="/journal/746">Integrated Computer-Aided Engineering</a></td><td>5.800</td><td>IOS Press</td></tr> <tr class="odd"> <td><a href="/journal/45">Journal of Network and Computer Applications</a></td><td>7.700</td><td>Elsevier</td></tr> <tr class="even"> <td><a href="/journal/1026">International Journal of Advances in Intelligent Informatics</a></td><td></td><td>Universitas Ahmad Dahlan</td></tr> </tbody> </table><div class="keys" style="display:none" title="/conference/478"><span>1122</span><span>408</span><span>53</span><span>966</span><span>1108</span><span>1002</span><span>832</span><span>746</span><span>45</span><span>1026</span></div> </div></div> </div> </div> <div class="portlet" id="yw8"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-eye-open"></i> Recommendation</div> </div> <div class="portlet-content"> <div align="center"> <script async src="//pagead2.googlesyndication.com/pagead/js/adsbygoogle.js"></script>  <ins class="adsbygoogle" style="display:block" data-ad-client="ca-pub-6743617430731451" data-ad-slot="1870704128" data-ad-format="autorelaxed"></ins> <script> (adsbygoogle = window.adsbygoogle || []).push({}); </script> </div> </div> </div> </div> <div class="span2 hidden-phone"> <div class="sidebar-nav"> <ul id="yw9"> <li><a 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