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)
Text
69292.pdf 319kB |
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: | Husmeier, Professor Dirk and Dondelinger, Mr Frank |
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 |
Related URLs: |
University Staff: Request a correction | Enlighten Editors: Update this record