The field of computational biology has seen dramatic growth over the past few years, both in terms of new available data, new scientific questions, and new challenges for learning and inference. In particular, biological data are often relationally structured and highly diverse, well-suited to approaches that combine multiple weak evidence from heterogeneous sources. These data may include sequenced genomes of a variety of organisms, gene expression data from multiple technologies, protein expression data, protein sequence and 3D structural data, protein interactions, gene ontology and pathway databases, genetic variation data (such as SNPs), and an enormous amount of textual data in the biological and medical literature. Furthermore, next generation sequencing technologies and high-throughput imaging techniques are yielding terabyte scale data sets that require novel algorithmic solutions. New types of scientific and clinical problems require the development of novel supervised and unsupervised learning methods that can use these growing resources.

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, and we will invite 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.