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Workshops on Machine Learning in Computational Biology (MLCB) & Machine Learning in Systems Biology (MLSB) 2015 @ Montreal

A workshop at the Annual Conference on Neural Information Processing Systems (NIPS 2015) @ Montreal, Date: December 12th 2015, Room: TBD

This year MLSB and MLCB are co-located at NIPS!

Important dates

  • Submission due Oct 4, 2015, 11:59pm (time zone of your choice)
  • Decision notifications: Oct 22, 2015 (tentative)
  • Workshop room: TBD

Workshop Description

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.

Invited Speakers

  • Dana Pe'er, Columbia University (USA)
  • Matthew Stephens, University of Chicago (USA)

Submission instructions

Researchers interested in contributing should upload an extended abstract of 4 pages in PDF format to the MLCB/MLSB submission web site

by Oct 4, 2015, 11:59pm (Samoa time time zone of your choice).

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 pt) and margins (1 in). Note that all submissions are required to be 4 pages but we allow the references to be 5th page. Authors can also include an appendix if deem necessary, however the reviewers are not required to review the appendices and therefore the appendix may not be consider in the review process.

Submissions should be suitably anonymized and meet the requirements for double-blind reviewing.

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

The workshop allows submissions of papers that are under review or have been recently published in a conference or a journal. Abstracts based on such papers should also comply with the 4 page limit. 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.

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
  • 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
  • ... and all the organizers (see below)


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