A temporal hidden Markov regression model for the analysis of gene regulatory networks.

Gupta, M. , Qu, P. and Ibrahim, J.G. (2007) A temporal hidden Markov regression model for the analysis of gene regulatory networks. Biostatistics, 8(4), pp. 805-820. (doi: 10.1093/biostatistics/kxm007)

Full text not currently available from Enlighten.

Abstract

We propose a novel hierarchical hidden Markov regression model for determining gene regulatory networks from genomic sequence and temporally collected gene expression microarray data. The statistical challenge is to simultaneously determine the groupings of genes and subsets of motifs involved in their regulation, when the groupings may vary over time, and a large number of potential regulators are available. We devise a hybrid Monte Carlo methodology to estimate parameters under 2 classes of latent structure, one arising due to the unobservable state identity of genes and the other due to the unknown set of covariates influencing the response within a state. The effectiveness of this method is demonstrated through a simulation study and an application on an yeast cell-cycle data set.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Gupta, Professor Mayetri
Authors: Gupta, M., Qu, P., and Ibrahim, J.G.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Biostatistics
ISSN:1465-4644
ISSN (Online):1468-4357
Published Online:30 March 2007

University Staff: Request a correction | Enlighten Editors: Update this record