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This article is part of the supplement: Neural Information Processing Systems (NIPS) workshop on New Problems and Methods in Computational Biology

Open Access Proceedings

NIPS workshop on New Problems and Methods in Computational Biology

Gal Chechik1*, Christina Leslie2, William Stafford Noble3, Gunnar Rätsch4, Quaid Morris5 and Koji Tsuda6

Author Affiliations

1 Computer Science Department, Stanford University, 353 Serra Mall, Stanford University, Stanford, CA 94305, USA

2 Computational Biology Program, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 460, New York, NY 10065, USA

3 Department of Genome Sciences, University of Washington, 1705 NE Pacific St, Seattle, WA 98109, USA

4 Friedrich Miescher Laboratory of the Max Planck Society, Spemannstr. 39, 72076 Tübingen, Germany

5 Terrence Donnelley Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario, M5S 3E1, Canada

6 Max Planck Institute for Biological Cybernetics, Spemannstr. 38, 72076 Tübingen, Germany

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BMC Bioinformatics 2007, 8(Suppl 10):S1  doi:10.1186/1471-2105-8-S10-S1

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/8/S10/S1


Published:21 December 2007

© 2007 Chechik et al; licensee BioMed Central Ltd.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Proceedings

December 8, 2006, Whistler, British Columbia, Canada

The field of computational biology has seen dramatic growth over the past few years, both in terms of available data, scientific questions and challenges for learning and inference. These new types of scientific and clinical problems require the development of novel supervised and unsupervised learning approaches. In particular, the field is characterized by a diversity of heterogeneous data. The human genome sequence is accompanied by real-valued gene expression data, functional annotation of genes, genotyping information, a graph of interacting proteins, a set of equations describing the dynamics of a system, localization of proteins in a cell, a phylogenetic tree relating species, natural language text in the form of papers describing experiments, partial models that provide priors, and numerous other data sources.

This supplementary issue consists of seven peer-reviewed papers based on the NIPS Workshop on New Problems and Methods in Computational Biology held at Whistler, British Columbia, Canada on December 8, 2006. The Neural Information Processing Systems Conference is the premier scientific meeting on neural computation, with session topics spanning artificial intelligence, learning theory, neuroscience, etc. The goal of this workshop was to present emerging problems and machine learning techniques in computational biology, with a particular emphasis on methods for computational learning from heterogeneous data.

We received 37 extended abstract submissions, from which 13 were selected for oral presentation. The current supplement contains seven papers based on a subset of the 13 extended abstracts. Submitted manuscripts were rigorously reviewed by at least two referees. The quality of each paper was evaluated with respect to its contribution to biology as well as the novelty of the machine learning methods employed.

Acknowledgements

We would like to thank the workshop presenters and participants who made this special issue possible. We gratefully acknowledge financial support from PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning), a European Network of Excellence (NoE).

Editors:

Gal Chechik, Google Research

Christina Leslie, Computational Biology Program, Memorial Sloan-Kettering Cancer Center

William Stafford Noble, Department of Genome Sciences, University of Washington

Gunnar Rätsch, Friedrich Miescher Laboratory of the Max Planck Society

Quaid Morris, Terrence Donnelley Centre for Cellular and Biomolecular Research, University of Toronto

Koji Tsuda, Max Planck Institute for Biological Cybernetics

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, UC San Diego

Brendan Frey, University of Toronto

Nir Friedman, Hebrew University and Harvard

Michael I. Jordan, UC Berkeley

Alexander Hartemink, Duke University

Michal Linial, The Hebrew University of Jerusalem

Klaus-Robert Müller, Fraunhofer FIRST

Uwe Ohler, Duke University

Alexander Schliep, Max Planck Institute for Molecular Genetics

Bernhard Schölkopf, Max Planck Institute for Biological Cybernetics

Eran Segal, Stanford University

Jean-Philippe Vert, Ecole des Mines de Paris

Additional Reviewers:

Asa Ben-hur, Tomer Hertz, Su-In Lee, Hiroshi Mamitsuka, Motoki Shiga, Haidong Wang

This article has been published as part of BMC Bioinformatics Volume 8 Supplement 10, 2007: Neural Information Processing Systems (NIPS) workshop on New Problems and Methods in Computational Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/8?issue=S10.