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February 2004 Computational Discovery of Gene Regulatory Binding Motifs: A Bayesian Perspective
Shane T. Jensen, X. Shirley Liu, Qing Zhou, Jun S. Liu
Statist. Sci. 19(1): 188-204 (February 2004). DOI: 10.1214/088342304000000107

Abstract

The Bayesian approach together with Markov chain Monte Carlo techniques has provided an attractive solution to many important bioinformatics problems such as multiple sequence alignment, microarray analysis and the discovery of gene regulatory binding motifs. The employment of such methods and, more broadly, explicit statistical modeling, has revolutionized the field of computational biology. After reviewing several heuristics-based computational methods, this article presents a systematic account of Bayesian formulations and solutions to the motif discovery problem. Generalizations are made to further enhance the Bayesian approach. Motivated by the need of a speedy algorithm, we also provide a perspective of the problem from the viewpoint of optimizing a scoring function. We observe that scoring functions resulting from proper posterior distributions, or approximations to such distributions, showed the best performance and can be used to improve upon existing motif-finding programs. Simulation analyses and a real-data example are used to support our observation.

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Shane T. Jensen. X. Shirley Liu. Qing Zhou. Jun S. Liu. "Computational Discovery of Gene Regulatory Binding Motifs: A Bayesian Perspective." Statist. Sci. 19 (1) 188 - 204, February 2004. https://doi.org/10.1214/088342304000000107

Information

Published: February 2004
First available in Project Euclid: 14 July 2004

zbMATH: 1057.62101
MathSciNet: MR2082154
Digital Object Identifier: 10.1214/088342304000000107

Keywords: Bayesian models , Gene regulation , Markov chain Monte Carlo , motif discovery , optimization , scoring functions

Rights: Copyright © 2004 Institute of Mathematical Statistics

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Vol.19 • No. 1 • February 2004
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