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
Full text not currently available from Enlighten.
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 |
University Staff: Request a correction | Enlighten Editors: Update this record