Crowdsourcing network inference: The DREAM predictive signaling network challenge

Prill, RJ; Saez-Rodriguez, J; Alexopoulos, LG; Sorger, PK; Stolovitzky, G

dc.contributor.author	Prill, RJ	en
dc.contributor.author	Saez-Rodriguez, J	en
dc.contributor.author	Alexopoulos, LG	en
dc.contributor.author	Sorger, PK	en
dc.contributor.author	Stolovitzky, G	en
dc.date.accessioned	2014-03-01T02:47:19Z
dc.date.available	2014-03-01T02:47:19Z
dc.date.issued	2011	en
dc.identifier.issn	1937-9145	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33073
dc.subject.other	accuracy	en
dc.subject.other	calculation	en
dc.subject.other	conference paper	en
dc.subject.other	cost	en
dc.subject.other	human	en
dc.subject.other	prediction	en
dc.subject.other	priority journal	en
dc.subject.other	protein analysis	en
dc.subject.other	protein interaction	en
dc.subject.other	protein phosphorylation	en
dc.subject.other	protein signaling network	en
dc.subject.other	proteomics	en
dc.subject.other	signal transduction	en
dc.subject.other	article	en
dc.subject.other	biology	en
dc.subject.other	cooperation	en
dc.subject.other	genetics	en
dc.subject.other	metabolism	en
dc.subject.other	methodology	en
dc.subject.other	protein protein interaction	en
dc.subject.other	phosphoprotein	en
dc.subject.other	Computational Biology	en
dc.subject.other	Cooperative Behavior	en
dc.subject.other	Phosphoproteins	en
dc.subject.other	Protein Interaction Maps	en
dc.subject.other	Proteomics	en
dc.subject.other	Signal Transduction	en
dc.title	Crowdsourcing network inference: The DREAM predictive signaling network challenge	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1126/scisignal.2002212	en
heal.identifier.secondary	http://dx.doi.org/10.1126/scisignal.2002212	en
heal.identifier.secondary	mr7	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	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.	en
heal.publisher	AMER ASSOC ADVANCEMENT SCIENCE	en
heal.journalName	Science Signaling	en
dc.identifier.doi	10.1126/scisignal.2002212	en
dc.identifier.isi	ISI:000294601900003	en
dc.identifier.volume	4	en
dc.identifier.issue	189	en