MLCB 2013 - 2013 NIPS Workshop on Machine Learning in Computational Biology

The field of computational biology has seen dramatic growth over the past few years, 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), cell images, 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 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.
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, and should not anonymize their paper in that case.
Invited Speakers
Jonathan Pritchard, Stanford (USA)
Samuel Kaski, HIIT (Finland)
Program Committee:
Babak Alipanahi, University of Toronto
Alexis Battle, Stanford
Karsten Borgwardt, Max Planck Institute
Gal Chechik, Gonda brain center, Bar Ilan University
Florence d'Alche-Buc, Université d'Evry-Val d'Essonne, Genopole
Alexander Hartemink, Duke University
Antti Honkela, University of Helsinki
Laurent Jacob, UC Berkeley
John Marioni, EMBL-EBI
Klaus-Robert Müller, Fraunhofer FIRST
William Noble, University of Washington
Yanjun Qi, NEC Labs America
Gunnar Rätsch, Sloan-Kettering Institute
Alexander Schliep, Rutgers University
Koji Tsuda, National Institute of Advanced Industrial Science and Technology (Japan)
... and all the organizers (see below)
Organizers
Anna Goldenberg, SickKids Research Institute program of Genetics and Genome Biology (Canada)
Sara Mostafavi, Stanford University (USA)
Oliver Stegle, EMBL (UK)
Jean-Philippe Vert, Mines ParisTech and Institut Curie (Paris, France)