Module networks revisited: computational assessment and prioritization of model predictions

TL;DR

This paper revisits module network inference in computational biology, proposing an ensemble-based approach that improves model robustness and better identifies condition-specific gene regulation compared to traditional optimization methods.

Contribution

It introduces an ensemble method for module network inference that enhances model accuracy and reliability over direct optimization techniques.

Findings

Ensemble approach yields more coherent gene modules.

Consistent regulator assignments are more literature-supported.

Ensemble averaging improves detection of condition-specific regulation.

Abstract

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. We revisit the approach of Segal et al. (2003) 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.