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NIPS workshop on New Problems and Methods in Computational Biology
BMC Bioinformatics volume 8, Article number: S1 (2007)
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.
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).
•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
•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
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.
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Chechik, G., Leslie, C., Noble, W.S. et al. NIPS workshop on New Problems and Methods in Computational Biology. BMC Bioinformatics 8, S1 (2007). https://doi.org/10.1186/1471-2105-8-S10-S1
- Computational Biology
- Machine Learning Technique
- Machine Learning Method
- Unsupervised Learning
- Heterogeneous Data