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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Publisher's URL: http://dx.doi.org/10.1155/2010/749848
Abstract
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 |
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Status: | Published |
Refereed: | Yes |
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: | 1687-8027 |
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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