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Machine Learning in Computational Biology


Rätsch,  G
Friedrich Miescher Laboratory, Max Planck Society;


Tsuda,  K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Chechik, G., Leslie, C., Noble, W., Rätsch, G., Morris, Q., & Tsuda, K. (2007). Machine Learning in Computational Biology.

Cite as: http://hdl.handle.net/21.11116/0000-0004-446F-4
The field of computational biology has seen dramatic growth over the past few years, both in terms of available data, scientific questions and challenges for learning and inference. These new types of scientific and clinical problems require the development of novel supervised and unsupervised learning approaches. In particular, the field is characterized by a diversity of heterogeneous data. The human genome sequence is accompanied by real-valued geneand protein expression data, functional annotation of genes, genotyping information, a graph of interacting proteins, a set of equations describing the dynamics of a system, local- ization of proteins in a cell, a phylogenetic tree relating species, natural language text in the form of papers describing experiments, partial models that provide priors, and numerous other data sources. The goal of this workshop is to present emerging problems and machine learning techniques in computa- tional biology, with a particular emphasis on methods for computational learning from heterogeneous data. The workshop includes invited and submitted talks from experts in the fields of biology, bioinformatics and machine learning. The topics range from case studies of particular biological problems to novel learning approaches in computational biology.