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 wellestablished inference methods often remain locked in their initial suboptimal solution. Results: We revisit the approach of Segal et al. (2003) to infer regulatory modules and their conditionspecific 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
This procedure is improved in REF using an ensemble of possible statistical models.
Module networks revisited: computational assessment and prioritization of model predictions
Bioinformatics
Bioinformatics
Computer Science Medicine Biology