Modelling nonstationary gene regulatory processes

3Citations
Citations of this article
28Readers
Mendeley users who have this article in their library.

This article is free to access.

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. © 2010 Marco Grzegorcyzk et al.

Cite

CITATION STYLE

APA

Grzegorcyzk, M., Husmeier, D., & Rahnenführer, J. (2010). Modelling nonstationary gene regulatory processes. Advances in Bioinformatics, 2010. https://doi.org/10.1155/2010/749848

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free