Machine Learning in Computational Biology (MLCB) 2010

Workshop Description

The field of computational biology has seen dramatic growth over the past few years, both in terms of new available data, new scientific questions, and new challenges for learning and inference. In particular, biological data are often relationally structured and highly diverse, well-suited to approaches that combine multiple weak evidence from heterogeneous sources. These data may include sequenced genomes of a variety of organisms, gene expression data from multiple technologies, protein expression data, protein sequence and 3D structural data, protein interactions, gene ontology and pathway databases, genetic variation data (such as SNPs), and an enormous amount of textual data in the biological and medical literature. New types of scientific and clinical problems require the development of novel supervised and unsupervised learning methods that can use these growing resources. Furthermore, next generation sequencing technologies are yielding terabyte scale data sets that require novel algorithmic solutions.

The goal of this workshop is to present emerging problems and machine learning techniques in computational biology. We invited several speakers from the biology/bioinformatics community who will present current research problems in bioinformatics, and we will invite contributed talks on novel learning approaches 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.

Program Committee (tentative)

• Florence d'Alche-Buc, Université d'Evry-Val d'Essonne, Genopole (France)
• Mathieu Blanchette, McGill University (Canada)
• Eleazar Eskin, UC Los Angeles (USA)
• Nir Friedman, The Hebrew University of Jerusalem (Israel)
• David Heckerman, Microsoft Research (USA)
• Michael I. Jordan, UC Berkeley (USA)
• Christina Leslie, Memorial Sloan-Kettering Cancer Research Center (USA)
• Michal Linial, The Hebrew University of Jerusalem (Israel)
• Quaid Morris, University of Toronto (Canada)
• Klaus-Robert Müller, Technical University Berlin (Germany)
• Dana Pe'er, Columbia University (USA)
• Uwe Ohler, Duke University (USA)
• Gunnar Rätsch, Friedrich Miescher Laboratory of the Max Planck Society (Germany)
• Alexander Schliep, Rutgers University (USA)
• Koji Tsuda, National Institute of Advanced Industrial Science and Technology (Japan)
• Eric Xing, Carnegie Mellon University (USA)
• ... and all the organizers (see below)

Organizers

• Tomer Hertz, Fred Hutchinson Cancer Research Center (USA)
• Yanjun Qi, Machine Learning Department, NEC Labs America (Princeton, USA)
• Jean-Philippe Vert, Centre for Computational Biology, Mines ParisTech (Fontainebleau, France)
• Gunnar Rätsch, Friedrich Miescher Laboratory, Max Planck Society (Tübingen, Germany)

MLCB 2009