<html><head><meta http-equiv="Content-Type" content="text/html charset=us-ascii"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">NIPS 2013 workshop on Machine Learning in Computational Biology<br>----------<br><br>Call for contributions<br><br> Workshop on Machine Learning in Computational Biology<br><br> <a href="http://www.mlcb.org/">http://www.mlcb.org</a><br><br><br> A workshop at the Twenty-Seventh Annual Conference on<br> Neural Information Processing Systems (NIPS 2013)<br> Lake Tahoe, Nevada, USA, December 10, 2013.<br><br><br>Important dates:<br>Oct 24, 2013 : Deadline for submission of extended abstracts<br>Nov 4, 2013: Acceptance notification<br>Dec 10, 2013: Workshop date<br><br>WORKSHOP DESCRIPTION<br><br>The field of computational biology has seen dramatic growth over<br>the past few years, in terms of newly available data, new<br>scientific questions and new challenges for learning and<br>inference. In particular, biological data is often relationally<br>structured and highly diverse, and thus requires combining multiple<br>weak evidence from heterogeneous sources. These sources include<br>sequenced genomes of a variety of organisms, gene expression data<br>from multiple technologies, protein sequence and 3D structural<br>data, protein interaction data, gene ontology and pathway<br>databases, genetic variation data (such as SNPs), high-content<br>phenotypic screening data, and an enormous<br>amount of text data in the biological and medical literature. New<br>types of scientific and clinical problems require novel<br>supervised and unsupervised learning approaches that can use these<br>growing resources. Furthermore, next generation sequencing<br>technologies are yielding terabyte scale data sets that require<br>novel algorithmic solutions.<br><br>The workshop will host presentations of emerging problems and<br>machine learning techniques in computational biology. We encourage<br>contributions describing either progress on new bioinformatics<br>problems or work on established problems using methods that are<br>substantially different from standard approaches. Kernel methods,<br>graphical models, semi-supervised approaches, feature selection<br>and other techniques applied to relevant bioinformatics problems<br>would all be appropriate for the workshop.<br><br>SUBMISSION INSTRUCTIONS<br><br>Researchers interested in contributing should upload an extended<br>abstract of 4 pages in PDF format to the MLCB submission web site<br><br><a href="http://www.easychair.org/conferences/?conf=mlcb2013">http://www.easychair.org/conferences/?conf=mlcb2013</a><br><br>by Oct 24, 2013, 11:59pm (time zone of your choice).<br><br>No special style is required. Authors may use the NIPS style file, but<br>are also free to use other styles as long as they use standard font<br>size (11 pt) and margins (1 in).<br><br>*Submissions should be suitably anonymized and meet the<br>requirements for double-blind reviewing.*<br><br>All submissions will be anonymously peer reviewed and will be<br>evaluated on the basis of their technical content. A strong<br>submission to the workshop typically presents a new learning method<br>that yields new biological insights, or applies an existing learning<br>method to a new biological problem. However, submissions that improve<br>upon existing methods for solving previously studied problems will<br>also be considered. Examples of research presented in previous years<br>can be found online at <a href="http://www.mlcb.org/nipscompbio/previous/">http://www.mlcb.org/nipscompbio/previous/</a>.<br><br>The workshop allows submissions of papers that are under review or<br>have been recently published in a conference or a journal. This is<br>done to encourage presentation of mature research projects that are<br>interesting to the community. The authors should clearly state any<br>overlapping published work at time of submission.<br><br>INVITED SPEAKERS<br><br>Jonathan Pritchard (Stanford)<br>Samuel Kaski (HIIT)<br><br><br><br>ORGANIZERS<br><br>Anna Goldenberg (University of Toronto)<br>Sara Mostafavi (Stanford)<br>Oliver Stegle (EMBL)<br>Jean-Philippe Vert (Mines ParisTech, Institut Curie)</body></html>