Inference of temporally varying Bayesian networks.
BACKGROUND
When analysing gene expression time series data, an often overlooked but crucial aspect of the model is that the regulatory network structure may change over time. Although some approaches have addressed this problem previously in the literature, many are not well suited to the sequential nature of the data.


RESULTS
Here, we present a method that allows us to infer regulatory network structures that may vary between time points, using a set of hidden states that describe the network structure at a given time point. To model the distribution of the hidden states, we have applied the Hierarchical Dirichlet Process Hidden Markov Model, a non-parametric extension of the traditional Hidden Markov Model, which does not require us to fix the number of hidden states in advance. We apply our method to existing microarray expression data as well as demonstrating is efficacy on simulated test data.
Centre of Integrative Systems Biology and Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. thomas.thorne@imperial.ac.uk
Thomas
T
Thorne
Michael P H
MP
Stumpf
D000818:Animals; D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism; D001499:Bayes Theorem; D004331:Drosophila melanogaster / Q000235:genetics / Q000254:growth & development / Q000378:metabolism; D015870:Gene Expression; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D008390:Markov Chains; D013213:Starch / Q000378:metabolism
D013213:Starch