MLCB 2009 “New Problems and Methods in Computational Biology”, Call for Contribution
Call for contributions
New Problems and Methods in Computational Biology
A workshop at the Twenty-Third Annual Conference on Neural Information Processing Systems (NIPS 2009) Whistler, BC, Canada, December 11 or 12, 2009.
Deadline for submission of extended abstracts: September 27, 2009,
The field of computational biology has seen dramatic growth over the past few years, in terms of newly available data, new scientific questions and new challenges for learning and inference. In particular, biological data is often relationally structured and highly diverse, and thus requires combining multiple weak evidence from heterogeneous sources. These sources include sequenced genomes of a variety of organisms, gene expression data from multiple technologies, protein sequence and 3D structural data, protein interaction data, gene ontology and pathway databases, genetic variation data (such as SNPs), and an enormous amount of text data in the biological and medical literature. These new types of scientific and clinical problems require novel
supervised and unsupervised learning approaches that can use these growing resources.
The workshop will host presentations of emerging problems and machine learning techniques 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, semi-supervised approaches, feature selection and other techniques applied to relevant bioinformatics problems
would all be appropriate for the workshop.
Researchers interested in contributing should upload an extended abstract of 1-6 pages in PDF format to the MLCB submission web site http://www.easychair.org/conferences/?conf=mlcb2009 by September 27, 2009, 11:59pm (Samoa time).
No special style is required. Authors may use the NIPS style file, but are also free to use other styles as long as they use standard font size (11-12 pt) and margins (1 in).
All submissions will be anonymously peer reviewed and will be evaluated on the basis of their technical content. A strong submission to the workshop typically presents a new learning method
that yields new biological insights, or applies an existing learning method to a new biological problem. However, submissions that improve upon existing methods for solving previously studied problems will also be considered. Examples of research presented in previous years
can be found online at http://www.mlcb.org/nipscompbio/previous/.
Please note that accepted abstracts will be posted online at www.mlcb.org. Authors may submit two versions of their abstract, a longer version for review and a shorter version for posting to the web page. In addition, presentations will be video taped and published online as part of the videolectures.net website supported by Pascal.
The workshop allows submissions of papers that are under review or have been recently published in a conference or a journal. This is done to encourage presentation of mature research projects that are interesting to the community. The authors should clearly state any overlapping published work at time of submission. Authors of accepted abstracts will be invited to submit full length versions of their contributions for publication in a special issue of BMC
Fred Hutchinson Cancer Research Center
William Stafford Noble,
Department of Genome Sciences, University of Washington
Machine Learning Department, NEC Research
Mines ParisTech, Institut Curie
Mathieu Blanchette, McGill University
Florence d’Alche-Buc, Université d’Evry-Val d’Essonne, Genopole,
Eleazar Eskin, UC Los Angeles,
Nir Friedman, The Hebrew University of Jerusalem ,
David Heckerman, Microsoft Research ,
Michael I. Jordan, UC Berkeley ,
Christina Leslie, Memorial Sloan-Kettering Cancer Research Center,
Michal Linial, The Hebrew University of Jerusalem ,
Quaid Morris, University of Toronto,
Klaus-Robert Müller, Fraunhofer FIRST ,
Dana Pe’er, Columbia University ,
Uwe Ohler, Duke University ,
Günnar Rätsch, Friedrich Miescher Laboratory of the Max Planck Society,
Alexander Schliep, Rutgers University,
Koji Tsuda, Computational Biology Research Center
Eric Xing, Carnegie-Mellon University ,