Bayesian modeling and inference for motif discovery

Gupta, M. and Liu, J.S. (2006) Bayesian modeling and inference for motif discovery. In: Do, K.-A., Müller, P. and Vannucci, M. (eds.) Bayesian Inference for Gene Expression and Proteomics. Cambridge University Press: Cambridge, UK. ISBN 9780521860925

Abstract

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

• Item Type: Book Sections
• Status: Published
• Glasgow Author(s) Enlighten ID: Gupta, Professor Mayetri
• Authors: Gupta, M., and Liu, J.S.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Publisher: Cambridge University Press
• ISBN: 9780521860925