Machine Learning in Computational Biology Workshop

The field of computational biology has seen dramatic growth over the past few years. A wide range of high-throughput technologies developed in the last decade now enable us to measure parts of a biological system at various resolutions—at the genome, epigenome, transcriptome, and proteome levels. These technologies are now being used to collect data for an ever-increasingly diverse set of problems, ranging from classical problems such as predicting differentially regulated genes between time points and predicting subcellular localization of RNA and proteins, to models that explore complex mechanistic hypotheses bridging the gap between genetics and disease, population genetics and transcriptional regulation. Fully realizing the scientific and clinical potential of these data requires developing novel supervised and unsupervised learning methods that are scalable, can accommodate heterogeneity, are robust to systematic noise and confounding factors, and provide mechanistic insights.

The goals of this workshop are to i) present emerging problems and innovative machine learning techniques in computational biology, and ii) generate discussion on how to best model the intricacies of biological data and synthesize and interpret results in light of the current work in the field. We will invite several rising leaders from the biology/bioinformatics community who will present current research problems in computational biology and lead these discussions based on their own research and experiences. We will also have the usual rigorous screening of 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 established alternatives. Kernel methods, graphical models, feature selection, non-parametric models and other techniques applied to relevant bioinformatics problems would all be appropriate for the workshop. We are particularly keen on considering contributions related to the prediction of functions from genotypes and that target data generated from novel technologies such as gene editing and single cell genomics, though we will consider all submissions that highlight applications of machine learning into computational biology. The targeted audience are people with interest in learning and applications to relevant problems from the life sciences, including NIPS participants without any existing research link to computational biology.

Organizers

• Nicolo Fusi, Microsoft Research, Cambridge (USA)
• Anna Goldenberg, SickKids Research Institute program of Genetics and Genome Biology (Canada)
• Sara Mostafavi, University of British Columbia (Canada)
• Gerald Quon, MIT, Cambridge (USA)
• Oliver Stegle, EMBL (UK)

Program Committee

• Alexis Battle, JHU
• Michael A. Beer, JHU
• Andreas Beyer, TU Dresden
• Karsten Borgwardt, ETH Zurich
• Gal Chechik, Gonda brain center, Bar Ilan University
• Chao Cheng, Dartmouth Medical School
• Manfred Claassen, ETH Zurich
• Florence d'Alche-Buc, Université d'Evry-Val d'Essonne, Genopole
• Saso Dzeroski, Jozef Stefan Institute
• Jason Ernst , UCLA
• Pierre Geurts, University of Liège
• James Hensman, The University of Sheffield
• Antti Honkela, University of Helsinki
• Laurent Jacob, Mines Paris Tech
• Samuel Kaski, Aalto University
• Seyoung Kim, CMU
• David Knowles, Stanford
• Anshul Kundaje, Stanford
• Neil Lawrence, University of Sheffield
• Su-In Lee, University of Washington
• Shen Li, Mount Sinai, New York
• Michal Linial, Hebrew University
• John Marioni, EMBL-EBI
• Martin Renqiang Min, NEC Labs America
• Yves Moreau, KU Leuven
• Alan Moses, University of Toronto
• Bernard Ng, UBC
• William Noble, University of Washington
• Uwe Ohler, MDC Berlin & Humboldt University
• Yongjin Park, MIT
• Leopold Parts, University of Toronto
• Dana Pe'er, Columbia University
• Nico Pfeifer, Max Planck Institute
• Magnus Rattray, University of Manchester
• Simon Rogers, University of Glasgow
• Juho Rousu, Aalto University
• Guido Sanguinetti, University of Edinburgh
• Alexander Schliep, Rutgers University
• Jean-Philippe Vert, Ecole des Mines de Paris
• Jinbo Xu, Toyota Technological Institute of Chicago
• Chun (Jimmie) Ye , UCSF