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Sci. Signal., 6 September 2011
Vol. 4, Issue 189, p. mr7
Crowdsourcing Network Inference: The DREAM Predictive Signaling Network Challenge
Robert J. Prill1,2*,
Leonidas G. Alexopoulos4,
Peter K. Sorger5,6, and
1 IBM Computational Biology Center, Yorktown Heights, NY, 10598, USA. 2 European Bioinformatics Institute (EMBL-EBI), Welcome Trust Genome Campus, Cambridge CB10 1SD, UK. 3 European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, D-69117 Heidelberg, Germany. 4 Department of Mechanical Engineering, National Technical University of Athens, Heroon Polytechniou 9, 15780 Zografou, Athens, Greece. 5 Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. 6 Department of Biological Engineering, MIT, Cambridge, MA 02139, USA.
* These authors contributed equally to this work.
Meeting Information: The DREAM4 Predictive Signaling Network Challenge took place in the summer of 2009. Results were presented at the DREAM4 conference, December 2009, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
Computational analyses of systematic measurements on the states and activities of signaling proteins (as captured by phosphoproteomic data, for example) have the potential to uncover uncharacterized protein-protein interactions and to identify the subset that are important for cellular response to specific biological stimuli. However, inferring mechanistically plausible protein signaling networks (PSNs) from phosphoproteomics data is a difficult task, owing in part to the lack of sufficiently comprehensive experimental measurements, the inherent limitations of network inference algorithms, and a lack of standards for assessing the accuracy of inferred PSNs. A case study in which 12 research groups inferred PSNs from a phosphoproteomics data set demonstrates an assessment of inferred PSNs on the basis of the accuracy of their predictions. The concurrent prediction of the same previously unreported signaling interactions by different participating teams suggests relevant validation experiments and establishes a framework for combining PSNs inferred by multiple research groups into a composite PSN. We conclude that crowdsourcing the construction of PSNs—that is, outsourcing the task to the interested community—may be an effective strategy for network inference.
Receptor Tyrosine Kinases Fall into Distinct Classes Based on Their Inferred Signaling Networks.
J. P. Wagner, A. Wolf-Yadlin, M. Sevecka, J. K. Grenier, D. E. Root, D. A. Lauffenburger, and G. MacBeath (2013)
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Systematic Analysis of Challenge-Driven Improvements in Molecular Prognostic Models for Breast Cancer.
A. A. Margolin, E. Bilal, E. Huang, T. C. Norman, L. Ottestad, B. H. Mecham, B. Sauerwine, M. R. Kellen, L. M. Mangravite, M. D. Furia, et al. (2013)
Science Translational Medicine
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An effective framework for reconstructing gene regulatory networks from genetical genomics data.