Module networks revisited: computational assessment and prioritization of model predictions

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Abstract
Motivation: 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. Availability: All software developed for this study is available from http://bioinformatics.psb.ugent.be/software. Contact:tom.michoel@psb.ugent.be Supplementary information:Supplementary data and figures are available from http://bioinformatics.psb.ugent.be/supplementary_data/anjos/module_nets_yeast/.
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