DREAM3 In-Silico Network Challenge (DREAM3, Challenge 4)
The goal of the in silico challenges is the reverse engineering of gene networks from steady state and time series data. Participants are challenged to predict the directed unsigned network topology from the given in silico generated gene expression datasets.
About the Data
These challenges have been provided by Daniel Marbach and his colleagues from the Laboratory of Intelligent Systems of the Swiss Federal Institute of Technology in Lausanne. The data can be freely used. Please cite the following papers in your publications:
- Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, and Stolovitzky G. Revealing strengths and weaknesses of methods for gene network inference. PNAS, 107(14):6286-6291, 2010. [PNAS] [info] [bibtex]
- Marbach D, Schaffter T, Mattiussi C, and Floreano D. Generating Realistic in silico Gene Networks for Performance Assessment of Reverse Engineering Methods. Journal of Computational Biology, 16(2) pp. 229-239, 2009. [info] [bibtex]
- Prill RJ, Marbach D, Saez-Rodriguez J, Sorger PK, Alexopoulos LG, Xue X, Clarke ND, Altan-Bonnet G, and Stolovitzky G. Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS ONE, 5(2):e9202, 2010. [PLoS] [bibtex]
- bteam: Kevin Yip, Roger Alexander, Koon-Kiu Yan, and Mark Gerstein, Yale University
Results & Additional Information
The challenge of size 10 had 29 participants, the one of size 50 had 27 participants, and the one of size 100 had 22 participants. This makes these challenges currently the most widely used gene network reverse engineering benchmark.
The challenges have been generated with GeneNetWeaver (GNW). GNW allows one to easily generate additional benchamarks of the same type as the DREAM3 in silico challenges. GNW is available open source at: gnw.sourceforge.net.
Additional information (the datasets without noise, the signed network structures, etc.) is available at: DREAM3 in silico challenge additional information.