Machine Learning in Computational Biology
We are excited to be holding the 14th Machine Learning in Computational Biology (MLCB) meeting, co-located with NeurIPS in Vancouver. Since its inception in 2004, and until 2017, MLCB was an official NeurIPS workshop (previous meetings 2004-2017). Given the growth and maturity of the field, this year MLCB will be an independent conference co-located with NeurIPS. Our sponsors include Recursion, Deep Genomics, and Amazon.
Annual MLCB Meeting 2019
- Time: December 13th - 14th, 2019 (starting at 4pm on the 13th)
- Location: Room C300 at UBC Robson Square (800 Robson St, Vancouver, BC V6Z 3B7; 10 mins by walk from the Convention Center)
- Registration: MLCB registration is free and does not require paper submission. Register here.
- Important dates and submission information
- Invited speakers' bios and abstracts
- Daphne Koller - Insitro (USA) "Machine learning: A new approach to drug discovery"
- Jennifer Listgarten - UC Berkeley (USA) "Machine learning for protein engineering"
- Quaid Morris - University of Toronto (Canada) "How to be a machine learning biologist"
- William Stafford Noble - University of Washington (USA) "Machine learning methods for making sense of big genomic and proteomic data"
Scope of MLCB
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 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.
In addition to talks by invited speakers, will also have the usual rigorous screening of contributed talks on novel learning approaches in computational biology. 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.