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Abstract

The field of computational biology has seen dramatic growth The field of computational biology has seen dramatic growth over the past few years, in terms of newly available data, new scientific questions and new challenges for learning and inference. In particular, biological data is often relationally structured and highly diverse, and thus requires combining multiple weak evidence from heterogeneous sources. These sources include sequenced genomes of a variety of organisms, gene expression data from multiple technologies, protein sequence and 3D structural data, protein interaction data, gene ontology and pathway databases, genetic variation data (such as SNPs), high-content phenotypic screening data, and an enormous amount of text data in the biological and medical literature. These new types of scientific and clinical problems require novel supervised and unsupervised learning approaches that can use these growing resources. The goal of this workshop is to present emerging problems and machine learning techniques in computational biology. During the workshop the audience will hear about the progress on new bioinformatics problems and new methodology for established problems. The targeted audience are people with interest in learning and applications to relevant problems from the life sciences.