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Mini Symposium
Machine Learning in Computational Biology
Yanjun Qi · Jean-Philippe Vert · Gal Chechik · Alexander Zien · Tomer Hertz · William S Noble

Thu Dec 10 01:30 PM -- 04:30 PM (PST) @ Regency C
Event URL: http://www.fml.tuebingen.mpg.de/nipscompbio/mlcb-2009/mini-symposium-program/ »

The field of computational biology has seen dramatic growth over the past fewyears, both in terms of new available data, new scientific questions, and newchallenges for learning and inference. In particular, biological data are oftenrelationally structured and highly diverse, well-suited to approaches thatcombine multiple weak evidence from heterogeneous sources. These data mayinclude sequenced genomes of a variety of organisms, gene expression data frommultiple technologies, protein expression data, protein sequence and 3Dstructural data, protein interactions, gene ontology and pathway databases,genetic variation data (such as SNPs), and an enormous amount of textual datain the biological and medical literature. New types of scientific and clinicalproblems require the development of novel supervised and unsupervised learningmethods that can use these growing resources. Furthermore, next generationsequencing technologies are yielding terabyte scale data sets that requirenovel algorithmic solutions. The goal of this min-symposium is to presentemerging problems and machine learning techniques in computational biology.

Author Information

Yanjun Qi (University of Virginia)
Jean-Philippe Vert (Google)
Gal Chechik (NVIDIA, BIU)
Alexander Zien (LIFE Biosystems GmbH)
Tomer Hertz (Fred Hutchnison Cancer Research Center)
William S Noble (University of Washington)

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