Modelling nonstationary gene regulatory processes

Grzegorcyzk, M., Husmeier, D. and Rahnenführer, J. (2010) Modelling nonstationary gene regulatory processes. Advances in Bioinformatics, 2010, pp. 1-17. (doi: 10.1155/2010/749848)

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An important objective in systems biology is to infer gene regulatory networks from postgenomic data, and dynamic Bayesian networks have been widely applied as a popular tool to this end. The standard approach for nondiscretised data is restricted to a linear model and a homogeneous Markov chain. Recently, various generalisations based on changepoint processes and free allocation mixture models have been proposed. The former aim to relax the homogeneity assumption, whereas the latter are more flexible and, in principle, more adequate for modelling nonlinear processes. In our paper, we compare both paradigms and discuss theoretical shortcomings of the latter approach. We show that a model based on the changepoint process yields systematically better results than the free allocation model when inferring nonstationary gene regulatory processes from simulated gene expression time series. We further cross-compare the performance of both models on three biological systems: macrophages challenged with viral infection, circadian regulation in Arabidopsis thaliana, and morphogenesis in Drosophila melanogaster.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk
Authors: Grzegorcyzk, M., Husmeier, D., and Rahnenführer, J.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Advances in Bioinformatics
ISSN (Online):1687-8035
Published Online:01 January 2010
Copyright Holders:Copyright © 2010 Marco Grzegorcyzk et al.
First Published:First published in Advances in Bioinformatics 2010:1-17
Publisher Policy:Reproduced under a Creative Commons Licence
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