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Biostatistics Advance Access originally published online on March 30, 2007
Biostatistics 2007 8(4):805-820; doi:10.1093/biostatistics/kxm007
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© The Author 2007. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

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

Mayetri Gupta*, Pingping Qu and Joseph G. Ibrahim

Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA gupta{at}bios.unc.edu

* To whom correspondence should be addressed.

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.

Keywords: Data augmentation; Gene expression; Transcription factor; Variable selection

Received May 15, 2006; revised December 1, 2006; accepted for publication February 19, 2007.


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