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. The goal of this workshop is to present emerging problems and machine learning techniques in computational biology. The workshop will include invited and submitted talks from experts in the fields of biology, bioinformatics and machine learning. We encourage contributions describing either progress on new biological problems or work on established problems using methods that are substantially different from standard approaches. Deadline for contributed abstracts: October 31, 2006.