The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Revealing strengths and weaknesses of methods for gene network inference
Open accessAbstract
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.
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Cited by
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“This suggests that the performance of GENIE3 at retrieving a regulator of one gene degrades as the number of regulators of this gene increases, as also observed from the analysis of the results of the DREAM3 challenge in [42].”
ResultWisdom of crowds for robust gene network inference
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Metadata
- Authors
- Marbach, D., Prill, R. J., Schaffter, T., Mattiussi, C., Floreano, D., & Stolovitzky, G.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2010-03-22
- Volume
- 107
- Issue
- 14
- Pages
- 6286-6291
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2161922735
- Cited by
- 752
- References
- 5
- FWCI
- 23.28
- Type
- journal article
- Language
- en
- Open access
- green
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