Machine Learning in Computational Biology (MLCB) 2014 @ Montreal
A workshop at the Annual Conference on Neural Information Processing Systems (NIPS 2014) @ Montreal, Date: Saturday Dec 13, Room: TBD
- Deadline extended: submission due Oct 22, 2014, 11:59pm (time zone of your choice)
- Decision notifications: Nov 4, 2014 (tentative)
- Workshop room: TBD
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.
- Mark Gerstein, Yale University (USA)
- Title: TBD.
- Abstract: TBD.
- Anshul Kundaje, Stanford University (USA)
- Title: TBD
Researchers interested in contributing should upload an extended abstract of 4 pages in PDF format to the MLCB submission web siteby Oct 22, 2014, 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. 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.
- Anna Goldenberg, SickKids Research Institute program of Genetics and Genome Biology (Canada)
- Sara Mostafavi, University of British Columbia (Canada)
- Oliver Stegle, EMBL (UK)
- Su-In Lee, University of Washington, Seattle (USA)
- Martin Min, NEC Labs, Princeton (USA)
These pages are kindly hosted by the Rätschlab.