Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series

Dondelinger, F., Husmeier, D. and Lebre, S. (2012) Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series. Euphytica, 183(3), pp. 361-377. (doi:10.1007/s10681-011-0538-3)

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Abstract

To understand the processes of growth and biomass production in plants, we ultimately need to elucidate the structure of the underlying regulatory networks at the molecular level. The advent of high-throughput postgenomic technologies has spurred substantial interest in reverse engineering these networks from data, and several techniques from machine learning and multivariate statistics have recently been proposed. The present article discusses the problem of inferring gene regulatory networks from gene expression time series, and we focus our exposition on the methodology of Bayesian networks. We describe dynamic Bayesian networks and explain their advantages over other statistical methods. We introduce a novel information sharing scheme, which allows us to infer gene regulatory networks from multiple sources of gene expression data more accurately. We illustrate and test this method on a set of synthetic data, using three different measures to quantify the network reconstruction accuracy. The main application of our method is related to the problem of circadian regulation in plants, where we aim to reconstruct the regulatory networks of nine circadian genes in Arabidopsis thaliana from four gene expression time series obtained under different experimental conditions.

Item Type:Articles
Additional Information:The original publication is available at www.springerlink.com
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Dondelinger, Mr Frank and Husmeier, Professor Dirk
Authors: Dondelinger, F., Husmeier, D., and Lebre, S.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Euphytica
Publisher:Springer
ISSN:0014-2336
ISSN (Online):1573-5060
Published Online:15 October 2011
Copyright Holders:Copyright © 2012 Springer Science and Business Media
First Published:First published in Euphytica 2012 183(3):361-377
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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