Connectionists: Program announced: NIPS-08 Computational biology workshop - Dec 11, 12

[Gal Chechik]

   The 6th workshop on Machine Learning in Computational Biology

                                 http://www.mlcb.org

     A workshop at the Twenty-Second Annual Conference on
         Neural Information Processing Systems (NIPS 2008)
        Workshop: Whistler, BC, Canada, December 12, 2008.
      Symposium: Vancouver, BC, Canada, December 11, 2008.

December 11, 2008, 13:00–16:00   Mini Symposium 3 13:00-14:00 *Modular
Biology: the Function and Evolution of Molecular
Networks<http://www.fml.tuebingen.mpg.de/nipscompbio/workshop-program#regev>
*                   Aviv Regev <http://www.broad.mit.edu/node/541>, Broad
Institute of MIT & Harvard, Cambridge, MA, USA
14:00-15:00 *Computational Studies Discover an new Mode of Gene
Regulation<http://www.fml.tuebingen.mpg.de/nipscompbio/workshop-program#brenner>
*                   Steven
Brenner<http://compbio.berkeley.edu/people/brenner/>,
University of California, Berkely, CA, USA
15:00-16:00 *Statistical Models for Predicting HIV
Phenotypes<http://www.fml.tuebingen.mpg.de/nipscompbio/workshop-program#lengauer>
*                   Thomas
Lengauer<http://bioinf.mpi-inf.mpg.de/homepage/index.php?&account=lengauer>,
Max Planck Insitute for Informatics, Saarbrücken, Germany December 12, 2008,
07:45–10:30 and 15:45–18:30 Hilton: Mt. Currie Morning session 7.45-8.10:
*Learning Temporal Sequence of Biological Networks, *                   Le
Song and Eric Xing 8.10-8.35: *   Switching Regulatory Models of Cellular
Stress Response,***                   Guido Sanguinetti, Andreas Ruttor,
Manfred Opper and Cedric Archambeau 8.35-9.00: *   Detecting the Presence
and Absence of Causal Relationships Between
**                   Expression of Yeast Genes with Very Few Samples *
Eun Yong Kang, Ilya Shpitser, Hyun Min Kang, Chun Ye and Eleazar Eskin
9.00-9.15    Coffee break
9.15-9.40*     KIRMES: Kernel-based Identification of Regulatory Modules in
**                   Euchromatic Sequences*                   Sebastian J.
Schultheiss, Wolfgang Busch, Jan Lohmann,
                   Oliver Kohlbacher and Gunnar RÃtsch 9.40-10.05: *
Approximate Substructure Matching for Biological Sequence
Classification*
Pavel Kuksa and Vladimir Pavlovic 10.05-10.30: *Predicting Binding
Affinities of MHC Class II Epitopes Across Alleles *                   Nico
Pfeifer and Oliver Kohlbacher
*Afternoon session*
3.45-4.10*     Inside the black box: Identifying causal genetic factors of
drug resistance*                    Bo-Juen Chen, Helen Causton, Ethan
Perlstein and Dana Peer 4.10-4.35     *Full Bayesian Survival Models for
Analyzing Human Breast Tumors*
                   Volker Roth, Thomas Fuchs, Sudhir Raman, Peter Wild,
                   Edgar Dahl and Joachim Buhmann 4.35-5.00 *
Probabilistic assignment of formulas to mass peaks in metabolomics
experiments***                   Simon Rogers, Richard A. Scheltema, Mark
Girolami and Rainer Breitling
5.00-5.15  Coffee break
5.15-5.40 *    Learning "graph-mer" motifs that predict gene expression
                   trajectories in development *                   Xuejing
Li, Chris Wiggins, Valerie Reinke and Christina Leslie 5.40-6.05     *On the
relationship between DNA periodicity and local chromatin structure *
Sheila Reynolds, Jeff Bilmes and William Stafford Noble
6.05-6.30     Discussion

WORKSHOP DESCRIPTION

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), 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, feature selection and other techniques applied to
relevant bioinformatics problems would all be appropriate for the
workshop.

This year the workshop will follow a mini-symposium on Computational
Biology that will take place in Vancouver on the afternoon of
Thursday Dec 11th.

ORGANIZERS

Gal Chechik,
        Google Research
Christina Leslie,
        Memorial Sloan-Kettering Cancer Center
William Stafford Noble,
        Department of Genome Sciences, University of Washington
Gunnar Raetsch, Friedrich Miescher Laboratory of the
        Max Planck Society (Tuebingen, Germany)
Quaid Morris, Terrence Donnelley Centre for Cellular and
        Biomolecular Research, University of Toronto


PROGRAM COMMITTEE

Pierre Baldi, UC Irvine
Kristin Bennett, Rensselaer Polytechnic Institute
Mathieu Blanchette, McGill University
Florence d'Alche, Universite d'Evry-Val d'Essonne, Genopole
Eleazar Eskin, UCLA
Brendan Frey, University of Toronto
Nir Friedman, The Hebrew University of Jerusalem
Michael I. Jordan, UC Berkeley
Alexander Hartemink, Duke University
David Heckerman, Microsoft Research
Michal Linial, The Hebrew University of Jerusalem
Klaus-Robert Mueller, Fraunhofer FIRST
Uwe Ohler, Duke University
Dana Pe'er, Columbia University
Alexander Schliep, Max Planck Institute for Molecular Genetics
Bernhard Schoelkopf, Max Planck Institute for Biological Cybernetics
Jean-Philippe Vert, Ecole des Mines de Paris
Eric Xing, Carnegie Mellon University
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