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Machine Learning in Computational Biology

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 is 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. 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 machine learning techniques in computational biology. We invited several speakers from the biology/bioinformatics community who presented current research problems in bioinformatics, and we invited contributed talks on novel learning approaches in computational biology. We encouraged contributions describing either progress on new bioinformatics problems or work on established problems using methods that are substantially different from standard approaches.

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