Module networks revisited: computational assessment and prioritization of model predictions

Anagha Joshi, Riet De Smet, Kathleen Marchal, Yves Van de Peer, Tom Michoel

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)490-496
Number of pages7
JournalBioinformatics
Volume25
Issue number4
DOIs
Publication statusPublished - 15 Feb 2009

Fingerprint

Dive into the research topics of 'Module networks revisited: computational assessment and prioritization of model predictions'. Together they form a unique fingerprint.

Cite this