Machine Learning in Computational Biology (MLCB) Meeting, co-located with NeurIPS.
Dec 13th and 14th 2019, Vancouver, Canada
We are excited to be holding the 14th MLCB meeting, co-located with NeurIPS in Vancouver. Since its inception in 2004, and until 2017, MLCB was an official NeurIPS workshop. Given the growth and maturity of the field, this year MLCB will be an independent conference co-located with NeurIPS.
Conference Location and Format
- The conference will be located downtown Vancouver (a few minute walks from the Convention Center-NeurIPS location) at UBC Robson Square.
- On Friday Dec 13th, we’ll have a full day of research talks and keynote presentations.
- On Saturday Dec 14th, we’ll be a hosting the first AI in Genomics Hackathon (AiGenHack)
- The meeting will be held in C300 on 13th (Theatre room), and C150/180 on Dec 14th for the Hackathon. Both at UBC Robson Square – 800 Robson St, Vancouver, BC V6Z 3B7.
- Submissions due: Sept 20th, 11:59pm (time zone of your choice) MLCB submission web site
- Decision notification: Oct 21th, 2019
- Workshop: Dec 13-14th, 2019
Researchers interested in contributing should upload an extended abstract of 4 pages (excluding references) in PDF format to the MLCB submission web site by Sep 20th, 2019, 11:59pm (time zone of your choice). Submissions exceeding the page limit will be automatically rejected.
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.
- Quaid Morris - University of Toronto (Canada)
- Jennifer Listgarten - UC Berkeley (USA)
- William Stafford Noble - University of Washington (USA)
- Daphne Koller - Insitro (USA)
- David Knowles - Columbia & New York Genome Center (USA)
- Anshul Kundaje - Stanford (USA)
- Su-In Lee - University Washington (USA)
- Sara Mostafavi - University of British Columbia (Canada)
- Gerald Quon - UC Davis (USA)
- James Zou - Stanford (USA)
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 leaders at the intersection of computational biology and machine learning 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. Deep learning, 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 will also encourage contributions to address new challenges in analyzing data generated from gene editing, single cell genomics and other novel technologies. The targeted audience are people with interest in machine learning and applications to relevant problems from the life sciences, including NIPS participants without any existing research link to computational biology. Many of the talks will be of interest to the broad machine learning community.