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 data are high-dimensional, heterogeneous, and are impacted by a range of confounding factors, presenting new challenges for standard learning and inference approaches. Therefore, fully realizing the scientific and clinical potential of these data requires development of 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 goal of this workshop is to present emerging problems and innovative machine learning techniques in computational biology. We will invite several speakers from the biology/bioinformatics community who will present current research problems in computational biology. 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. 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 are particularly keen on considering contributions related to the prediction of functions from genotypes and to applications in personalized medicine, as illustrated by our invited speakers. 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.