Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

DSpace/Manakin Repository

Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

Citable link to this page

. . . . . .

Title: Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges
Author: Prill, Robert J.; Marbach, Daniel; Saez-Rodriguez, Julio; Alexopoulos, Leonidas G.; Xue, Xiaowei; Clarke, Neil D.; Altan-Bonnet, Gregoire; Stolovitzky, Gustavo; Sorger, Peter Karl

Note: Order does not necessarily reflect citation order of authors.

Citation: Prill, Robert J., Daniel Marbach, Julio Saez-Rodriguez, Peter K. Sorger, Leonidas G. Alexopoulos, Xiaowei Xue, Neil D. Clarke, Gregoire Altan-Bonnet, and Gustavo Stolovitzky. 2010. Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges. PLoS ONE 5(2):e9202.
Full Text & Related Files:
Abstract: Background: 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. Methodology and Principal Findings: 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. Conclusions: 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.
Published Version: doi:10.1371/journal.pone.0009202
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2826397/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:7628414

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters