Crowdsourcing Network Inference: The DREAM Predictive Signaling Network Challenge

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Science Signaling  30 Aug 2011:

DOI: 10.1126/scisignal.2002212


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 are a difficult task, owing in part to the lack of sufficiently comprehensive experimental measurements, to the inherent limitations of network inference algorithms, and to 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 unlikely 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.

Full article available 6 September 2011, Vol. 4, Issue 189, pp. mr7

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