Revealing strengths and weaknesses of methods for gene network inference
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 p...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en_US |
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National Academy of Sciences
2011
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| Online Access: | http://hdl.handle.net/1721.1/61406 |
| _version_ | 1826213952224231424 |
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| author | Marbach, Daniel Schaffter, Thomas Prill, Robert J. Mattiussi, Claudio Floreano, Dario Stolovitzky, Gustavo |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Marbach, Daniel Schaffter, Thomas Prill, Robert J. Mattiussi, Claudio Floreano, Dario Stolovitzky, Gustavo |
| author_sort | Marbach, Daniel |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:57:27Z |
| format | Article |
| id | mit-1721.1/61406 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:57:27Z |
| publishDate | 2011 |
| publisher | National Academy of Sciences |
| record_format | dspace |
| spelling | mit-1721.1/614062022-10-02T05:19:30Z Revealing strengths and weaknesses of methods for gene network inference Marbach, Daniel Schaffter, Thomas Prill, Robert J. Mattiussi, Claudio Floreano, Dario Stolovitzky, Gustavo Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Marbach, Daniel Marbach, Daniel 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. National Institutes of Health (U.S.) (NIH Roadmap Initiative) Center for the Multiscale Analysis of Genomic and Cellular Networks Columbia University Swiss National Science Foundation (Grant no. 200021–112060) SystemsX.ch initiative (WingX project) IBM Computational Biology Center 2011-03-04T16:03:21Z 2011-03-04T16:03:21Z 2010-04 2009-11 Article http://purl.org/eprint/type/JournalArticle 0027-8424 1091-6490 http://hdl.handle.net/1721.1/61406 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. en_US http://dx.doi.org/10.1073/pnas.0913357107 Proceedings of the National Academy of Sciences of the United States of America 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. application/pdf National Academy of Sciences PNAS |
| spellingShingle | Marbach, Daniel Schaffter, Thomas Prill, Robert J. Mattiussi, Claudio Floreano, Dario Stolovitzky, Gustavo Revealing strengths and weaknesses of methods for gene network inference |
| title | Revealing strengths and weaknesses of methods for gene network inference |
| title_full | Revealing strengths and weaknesses of methods for gene network inference |
| title_fullStr | Revealing strengths and weaknesses of methods for gene network inference |
| title_full_unstemmed | Revealing strengths and weaknesses of methods for gene network inference |
| title_short | Revealing strengths and weaknesses of methods for gene network inference |
| title_sort | revealing strengths and weaknesses of methods for gene network inference |
| url | http://hdl.handle.net/1721.1/61406 |
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