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Proc Natl Acad Sci U S A. 2010 Apr 6;107(14):6286-91. doi: 10.1073/pnas.0913357107. Epub 2010 Mar 22.

Revealing strengths and weaknesses of methods for gene network inference.

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  • 1Laboratory of Intelligent Systems, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.

Abstract

Numerous methods have been developed for inferring gene regulatory networks from expression data, however, both their absolute and comparative performance remain poorly understood. In this paper, we introduce a framework for critical performance assessment of methods for gene network inference. We present an in silico benchmark suite that we provided as a blinded, community-wide challenge within the context of the DREAM (Dialogue on Reverse Engineering Assessment and Methods) project. We assess the performance of 29 gene-network-inference methods, which have been applied independently by participating teams. Performance profiling reveals that current inference methods are affected, to various degrees, by different types of systematic prediction errors. In particular, all but the best-performing method failed to accurately infer multiple regulatory inputs (combinatorial regulation) of genes. The results of this community-wide experiment show that reliable network inference from gene expression data remains an unsolved problem, and they indicate potential ways of network reconstruction improvements.

PMID:
20308593
[PubMed - indexed for MEDLINE]
PMCID:
PMC2851985
Free PMC Article

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