Revealing strengths and weaknesses of methods for gene network inference
Name
Marbach-2010-Revealing strengths.pdf
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452.92 KB
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Author(s)
Marbach, Daniel
Schaffter, Thomas
Prill, Robert J.
Mattiussi, Claudio
Floreano, Dario
Stolovitzky, Gustavo
Date Issued
April 2010
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
National Academy of Sciences
Citation
Marbach, Daniel et al. “Revealing strengths and weaknesses of methods for gene network inference.” Proceedings of the National Academy of Sciences 107.14 (2010): 6286 -6291. ©2010 by the National Academy of Sciences.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1073/pnas.0913357107
