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MLCB 2011 - Call for contribution

Call for contributions

Workshop on Machine Learning in Computational Biology

A workshop at the Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011) Sierra Nevada, Spain, December 16 or 17, 2011.

Deadline for submission of extended abstracts: Oct 20, 2011


The field of computational biology has seen dramatic growth over the past few years, in terms of newly available data, new scientific questions and new challenges for learning and inference. In particular, biological data is often relationally structured and highly diverse, and thus requires combining multiple weak evidence from heterogeneous sources. These sources include sequenced genomes of a variety of organisms, gene expression data from multiple technologies, protein sequence and 3D structural data, protein interaction data, gene ontology and pathway databases, genetic variation data (such as SNPs), high-content phenotypic screening data, and an enormous amount of text data in the biological and medical literature. These new types of scientific and clinical problems require novel supervised and unsupervised learning approaches that can use these growing resources.

The workshop will host presentations of emerging problems and machine learning techniques in computational biology. We encourage contributions describing either progress on new bioinformatics problems or work on established problems using methods that are substantially different from standard approaches. Kernel methods, graphical models, semi-supervised approaches, feature selection and other techniques applied to relevant bioinformatics problems would all be appropriate for the workshop.


Researchers interested in contributing should upload an extended abstract of 4 pages in PDF format to the MLCB submission web site
(ATT: we made a mistake in the email announcement of the submission URL. Here is the correct one. )

by Oct 20, 2011, 11:59pm (Samoa time).

No special style is required. Authors may use the NIPS style file, but are also free to use other styles as long as they use standard font size (11 pt) and margins (1 in).

All submissions will be anonymously peer reviewed and will be evaluated on the basis of their technical content. A strong submission to the workshop typically presents a new learning method that yields new biological insights, or applies an existing learning method to a new biological problem. However, submissions that improve upon existing methods for solving previously studied problems will also be considered. Examples of research presented in previous years can be found online at

Please note that accepted abstracts will be posted online at Authors may submit two versions of their abstract, a longer version for review and a shorter version for posting to the web page. In addition, we intent to make presentations be video taped and published online as part of the website supported by Pascal.

The workshop allows submissions of papers that are under review or have been recently published in a conference or a journal. This is done to encourage presentation of mature research projects that are interesting to the community. The authors should clearly state any overlapping published work at time of submission.


  • Anna Goldenberg, SickKids Research Institute program of Genetics and Genome Biology (Canada)
  • Tomer Hertz, Fred Hutchinson Cancer Research Center (USA)
  • Christina Leslie, Sloan-Kettering Institute (New York, USA)
  • Yanjun Qi, Machine Learning Department, NEC Labs America (Princeton, USA)
  • Gunnar Rätsch, Friedrich Miescher Laboratory, Max Planck Society (Tübingen, Germany)
  • Jean-Philippe Vert, Centre for Computational Biology, Mines ParisTech (Fontainebleau, France)


    • Karsten Borgwardt, Max Planck Institute
    • Gal Chechik, Gonda brain center, Bar Ilan University
    • Florence d'Alche-Buc, Université d'Evry-Val d'Essonne, Genopole
    • Nir Friedman, the Hebrew University of Jerusalem
    • Alexander Hartemink, Duke University
    • David Heckerman, Microsoft Research
    • Michael Jordan, UC Berkeley
    • Samuel Kaski, Helsinki Institute for Information Technology
    • Michal Linial, The Hebrew University of Jerusalem
    • Quaid Morris, University of Toronto
    • Klaus-Robert Müller, Fraunhofer FIRST
    • William Noble, University of Washington
    • Alexander Schliep, Rutgers University
    • Koji Tsuda, National Institute of Advanced Industrial Science and Technology (Japan)
    • Eric Xing, Carnegie Mellon University

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