NIPS MLCB 2017: NIPS Machine Learning in Computational Biology Workshop 2017 Long Beach Convention Center Long Beach, CA, United States, December 9, 2017 |
Conference website | https://mlcb.github.io/ |
Submission link | https://easychair.org/conferences/?conf=nipsmlcb2017 |
Submission deadline | October 15, 2017 |
NIPS 2017 workshop on Machine Learning in Computational Biology
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Call for contributions
https://mlcb.github.io
A workshop at the Twenty-Ninth Annual Conference on Neural Information Processing Systems (NIPS 2017)
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 increasingly diverse set of problems, ranging from classical problems such as predicting differentially regulated genes between time points and predicting subcellular localization of RNA, 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 into the underlying biological phenomena.
The goals of this workshop are to i) present emerging problems and innovative machine learning techniques in computational biology, and ii) generate discussion of how to best model the intricacies of biological data, and how to synthesize and interpret results in light of the current work in the both fields. We invited several rising leaders from the computational biology 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 problems or work on established problems using methods that are substantially different from established alternatives. Kernel methods, graphical models, feature selection, nonparametric models, and other techniques applied to relevant bioinformatics problems would all be appropriate submissions. 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.
Submission Guidelines
Researchers interested in contributing should upload an extended abstract of 4 pages in PDF format to the MLCB submission web site
https://easychair.org/conferences/?conf=nipsmlcb2017
by Oct 13, 2017, 11:59pm (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).
*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 at http://www.mlcb.org/nipscompbio/previous/.
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.
Committees
Organizing committee
- Nicolo Fusi - Microsoft Research (USA)
- Sara Mostafavi - University of British Columbia (Canada)
- Anshul Kundaje - Stanford (USA)
- Gerald Quon - UC Davis (USA)
- James Zou - Stanford (USA)
Invited Speakers
- Eran Halperin - UCLA (USA)
- Christina Leslie - Memorial Sloan Kettering Cancer Center (USA)
- Ben Raphael - Princeton (USA)
Venue
The workshop will be held in Long Beach, California, on December 9, 2017.