We’re extending the MLCB submission deadline to midnight PST on Friday 2nd October.
Machine Learning in Computational Biology (MLCB), Nov 23-24, 2020 We are excited to be holding the 15th Machine Learning in Computational Biology (MLCB) meeting. In its 2020 reincarnation, MLCB will be a two day virtual conference November 23 and 24, 9am-5pm PST. Since its inception in 2004, and until 2017, MLCB was an official NeurIPS workshop. Given the growth and maturity of the field, MLCB is now an independent meeting. More details can be found on the website: https://mlcb.github.io Registration: https://forms.gle/ZSwPFoLQ3iWBedsj8 Papers from MLCB 2019 can be viewed here: https://mlcb.github.io/mlcb2019_proceedings/ Important dates: Submissions due: Sept 30th October 2nd 11:59pm (PST) at https://cmt3.research.microsoft.com/mlcb2020 Decision notification: Oct 30th Meeting: Nov 23-24th. Keynote speakers Eran Segal (Weizmann Institute of Science) Aviv Regev (MIT, Broad Institute, Genentech) Regina Barzilay (MIT) Marinka Zitnik (Harvard Medical School) Organizers Anshul Kundaje - Stanford University (USA) Su-In Lee - University of Washington (USA) Sara Mostafavi - University of Washington (USA) Gerald Quon - UC Davis (USA) James Zou - Stanford University (USA) David A Knowles - Columbia University & New York Genome Center (USA) Meeting description The field of computational biology has seen dramatic growth over the past few years. A wide range of high-throughput omics and imaging 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 diverse technologies are now being used to study questions relevant to basic biology and human health. 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 the MLCB meeting 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. Submission instructions We will accept submission in two tracks: 1. Research 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. 2. Perspective on important but controversial problems. Examples include (but are not limited to) different ways of conceptualizing the supposed dichotomy between interpretation and accuracy, over-interpretation from visual representations of high dimensional data, lack of statistical rigour in inferring conclusions from data and models. Researchers interested in contributing should upload a short paper (4 pages) in PDF format to the MLCB submission web site: https://cmt3.research.microsoft.com/mlcb2020 by Sept 30th, 11:59 pm (PST). Both tracks will undergo rigorous peer review as in previous years. No special style is required. Authors may use the NeurIPS style file, but are also free to use other styles as long as they use standard font size (10 or 11pt) and margins (1 in). Papers need not be anonymized (but you can choose to anonymize your submission if you wish). All submissions to the Research track will be peer reviewed and will be evaluated on the basis of their technical content. A strong submission to the meeting 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://raetschlab.org:10080/nipscompbio/previous and https://mlcb.github.io/mlcb2019_proceedings/. The meeting 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 the time of submission (e.g. as a footnote on the first page). -- David A. Knowles, PhD. Core Faculty Member, New York Genome Center. Assistant Professor, Computer Science, Columbia University. Interdisciplinary Appointee, Systems Biology, Columbia University. Affiliate Member, Data Science Institute, Columbia University. https://daklab.github.io/
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