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Mini Symposium
Machine Learning in Computational Biology
Gal Chechik · Christina Leslie · Quaid Morris · William S Noble · Gunnar Rätsch

Thu Dec 11 01:30 PM -- 04:30 PM (PST) @
Event URL: http://www.mlcb.org »

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.

Author Information

Gal Chechik (NVIDIA, Bar-Ilan University)
Christina Leslie (Memorial Sloan Kettering Cancer Center)
Quaid Morris (Memorial Sloan Kettering)
William S Noble (University of Washington)
Gunnar Rätsch (ETHZ)

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