Machine Learning in Computational Biology (MLCB) 2015 @ Montreal
A workshop at the Annual Conference on Neural Information Processing Systems (NIPS 2015) @ Montreal, Date: December 12th 2015, Room: TBD
- Submission due Oct 4, 2015, 11:59pm (time zone of your choice)
- Decision notifications: Oct 22, 2015 (tentative)
- Workshop room: TBD
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.
- Dana Pe'er, Columbia University (USA)
- Matthew Stephens, University of Chicago (USA)
Researchers interested in contributing should upload an extended abstract of 4 pages in PDF format to the MLCB submission web siteby Oct 5, 2015, 11:59pm (
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).
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.
- Alexis Battle, JHU
- Andreas Beyer, TU Dresden
- Gal Chechik, Gonda brain center, Bar Ilan University
- Chao Cheng, Dartmouth Medical School
- Florence d'Alche-Buc, Université d'Evry-Val d'Essonne, Genopole
- Jason Ernst , UCLA
- Antti Honkela, University of Helsinki
- Laurent Jacob, Mines Paris Tech
- Seyoung Kim, CMU
- David Knowles, Stanford
- Anshul Kundaje, Stanford
- Neil Lawrence, University of Sheffield
- Shen Li, Mount Sinai, New York
- John Marioni, EMBL-EBI
- Bernard Ng, UBC
- William Noble, University of Washington
- Leopold Parts, University of Toronto
- Magnus Rattray, University of Manchester
- Alexander Schliep, Rutgers University
- ... and all the organizers (see below)
- 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)
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