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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.