Module networks revisited: computational assessment and prioritization of model predictions.
BACKGROUND
The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints, such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution.


RESULTS
We revisit the approach of Segal et al. to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution, we use a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data. The ensemble method automatically selects a subset of most informative genes and builds a quantitatively better model for them. Genes which cluster together in the majority of models produce functionally more coherent modules. Regulators which are consistently assigned to a module are more often supported by literature, but a single model always contains many regulator assignments not supported by the ensemble. Reliably detecting condition-specific or combinatorial regulation is particularly hard in a single optimum but can be achieved using ensemble averaging.


BACKGROUND
All software developed for this study is available from http://bioinformatics.psb.ugent.be/software.
Joshi
Anagha
A
Department of Plant Systems Biology, VIB, Ghent University, Technologiepark 927, B-9052 Gent, Belgium.
De Smet
Riet
R
Marchal
Kathleen
K
Van de Peer
Yves
Y
Michoel
Tom
T
D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D015233:Models, Statistical; D012984:Software