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Bayesian modeling and inference for motif discovery

By M. Gupta and J.S. Liu

Abstract

Motif discovery, which focuses on locating short sequence patterns associated\ud with the regulation of genes in a species, leads to a class of statistical missing\ud data problems. These problems are discussed first with reference to a\ud hypothetical model, which serves as a point of departure for more realistic\ud versions of the model. Some general results relating to modeling and inference\ud through the Bayesian and/or frequentist perspectives are presented, and\ud specific problems arising out of the underlying biology are discussed

Publisher: Cambridge University Press
Year: 2006
OAI identifier: oai:eprints.gla.ac.uk:69222
Provided by: Enlighten
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