Towards a rigorous assessment of systems biology models: the DREAM3 challenges.
<h4>Background</h4>Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments...
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| Language: | English |
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Public Library of Science (PLoS)
2010-02-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0009202&type=printable |
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| author | Robert J Prill Daniel Marbach Julio Saez-Rodriguez Peter K Sorger Leonidas G Alexopoulos Xiaowei Xue Neil D Clarke Gregoire Altan-Bonnet Gustavo Stolovitzky |
| author_facet | Robert J Prill Daniel Marbach Julio Saez-Rodriguez Peter K Sorger Leonidas G Alexopoulos Xiaowei Xue Neil D Clarke Gregoire Altan-Bonnet Gustavo Stolovitzky |
| author_sort | Robert J Prill |
| collection | DOAJ |
| description | <h4>Background</h4>Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges.<h4>Methodology and principal findings</h4>We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method.<h4>Conclusions</h4>DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature. |
| format | Article |
| id | doaj-art-71fe99880a5544e8b4f5c19f4adc914e |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2010-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-71fe99880a5544e8b4f5c19f4adc914e2025-08-20T03:07:40ZengPublic Library of Science (PLoS)PLoS ONE1932-62032010-02-0152e920210.1371/journal.pone.0009202Towards a rigorous assessment of systems biology models: the DREAM3 challenges.Robert J PrillDaniel MarbachJulio Saez-RodriguezPeter K SorgerLeonidas G AlexopoulosXiaowei XueNeil D ClarkeGregoire Altan-BonnetGustavo Stolovitzky<h4>Background</h4>Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges.<h4>Methodology and principal findings</h4>We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method.<h4>Conclusions</h4>DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0009202&type=printable |
| spellingShingle | Robert J Prill Daniel Marbach Julio Saez-Rodriguez Peter K Sorger Leonidas G Alexopoulos Xiaowei Xue Neil D Clarke Gregoire Altan-Bonnet Gustavo Stolovitzky Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS ONE |
| title | Towards a rigorous assessment of systems biology models: the DREAM3 challenges. |
| title_full | Towards a rigorous assessment of systems biology models: the DREAM3 challenges. |
| title_fullStr | Towards a rigorous assessment of systems biology models: the DREAM3 challenges. |
| title_full_unstemmed | Towards a rigorous assessment of systems biology models: the DREAM3 challenges. |
| title_short | Towards a rigorous assessment of systems biology models: the DREAM3 challenges. |
| title_sort | towards a rigorous assessment of systems biology models the dream3 challenges |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0009202&type=printable |
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