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A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data

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NIPS workshop on New Problems and Methods in

Computational Biology

December 18th, 2004, Whistler, British Columbia, Canada

Description:

This supplement issue consists of 10 peer-reviewed papers and one review

article based on the NIPS Workshop on New Problems and Methods in

Computational Biology held at Whistler, Canada on December 18th, 2004. This

workshop is designed to bring together machine learning and computational

biology researchers to develop fundamentally new methods for analyzing

biological data.

We received submissions both from the presenters at the workshop as well as

non-presenters. Submitted manuscripts were rigorously reviewed by at least two

referees. The quality of each paper was evaluated on the contributions to biology

(2)

conference is a leading machine learning conference, we required technical

novelty and mathematical rigor in methodology.

We would like to thank the workshop presenters and participants who made this

special issue possible. Special thanks go to the editors of BMC Bioinformatics

who advised us in preparing the manuscripts. Finally we acknowledge the

financial support by PASCAL (Pattern Analysis, Statistical Modelling and

Computational Learning,) a newly launched European Network of Excellence

(NoE).

Editors:

• Gal Chechik, Department of Computer Science, Stanford University

• Christina Leslie, Center for Computational Learning Systems, Columbia

University

• Gunnar Rätsch, Friedrich Miescher Laboratory of the Max Planck Society

• Koji Tsuda, AIST Computational Biology Research Center (Tokyo)

Program Committee:

(3)

• Kristin Bennett, Rensselaer Polytechnic Institute

• Nello Cristianini, UC Davis

• Eleazar Eskin, UC San Diego

• Nir Friedman, Hebrew University and Harvard

• Dan Geiger, The Technion

• Michael I. Jordan, UC Berkeley

• Alexander Hartemink, Duke University

• Klaus-Robert Müller, Fraunhofer FIRST

• William Stafford Noble, University of Washington

• Bernhard Schölkopf, Max Planck Institute for Biological Cybernetics

• Alexander Schliep, Max Planck Institute for Molecular Genetics

• Eran Segal , Stanford University

Referências

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