MLCB 2011 - Machine Learning in Computational Biology (MLCB) 2011

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. The targeted audience are people with interest in learning and applications to relevant problems from the life sciences.

Computational biology currently attracts great interest in the NIPS community, but there is still no yearly forum for advances in machine learning for computational biology within existing conferences in the two fields. Over the past few years, we have been working to establish this workshop as a recurring annual meeting in order to provide such a forum. In addition to having continuity among the organizers, we have enlisted a distinguished program committee to ensure that diverse work of the best quality is represented at the workshop. Typically, at least one invited speaker has been a prominent molecular biologist, with the goal of introducing the audience to emerging problems, technologies, and data sources from a biological viewpoint. We have previously organized BMC Bioinformatics special issues with work presented at the workshop, to increase the visibility of learning methods in computational biology. We have also attracted funding from the EU PASCAL2 network to support invited speakers and video recording of the talks for publication on http://videolectures.net.

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)
Dana Pe'er, Columbia University (USA)
Uwe Ohler, Duke University (USA)
Karsten Borgwardt, Max Planck Campus, Tübingen (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)

http://www.mlcb.org/