Revealing strengths and weaknesses of methods for gene network inference

Daniel Marbach, Robert J. Prill, Thomas Schaffter, Claudio Mattiussi, Dario Floreano, Gustavo Stolovitzky

Research output: Contribution to journalArticlepeer-review

564 Scopus citations


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, communitywide challenge within the context of the DREAM (Dialogue on Reverse Engineering Assessment and Methods) project. Weassess 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.

Original languageEnglish
Pages (from-to)6286-6291
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number14
StatePublished - 6 Apr 2010
Externally publishedYes


  • Community experiment
  • Dream challenge
  • Performance assessment
  • Reverse engineering
  • Transcriptional regulatory networks


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