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000008400 0247_ $$2DOI$$a10.1002/pmic.200900259
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000008400 084__ $$2WoS$$aBiochemical Research Methods
000008400 084__ $$2WoS$$aBiochemistry & Molecular Biology
000008400 1001_ $$0P:(DE-HGF)0$$aQi, Y.$$b0
000008400 245__ $$aSystematic prediction of human membrane   receptor interactions
000008400 260__ $$aWeinheim$$bWiley VCH$$c2009
000008400 300__ $$a5243 - 5255
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000008400 440_0 $$010401$$aProteomics$$v9$$x1615-9853$$y23
000008400 500__ $$aThis work was supported in part by National Science Foundation Grants ITR 0225656, CAREER 0448453 and CAREER CC044917, National Institutes of Health Grants NLM108730, R01 CA098372 and AI060422 and the Sofya Kovalvskaya Award (to J. K. S.) from the Humboldt Foundation.
000008400 520__ $$aMembrane receptor-activated signal transduction pathways are integral to cellular functions and disease mechanisms in humans. Identification of the full set of proteins interacting with membrane receptors by high-throughput experimental means is difficult because methods to directly identify protein interactions are largely not applicable to membrane proteins. Unlike prior approaches that attempted to predict the global human interactome, we used a computational strategy that only focused on discovering the interacting partners of human membrane receptors leading to improved results for these proteins. We predict specific interactions based on statistical integration of biological data containing highly informative direct and indirect evidences together with feedback from experts. The predicted membrane receptor interactome provides a system-wide view, and generates new biological hypotheses regarding interactions between membrane receptors and other proteins. We have experimentally validated a number of these interactions. The results suggest that a framework of systematically integrating computational predictions, global analyses, biological experimentation and expert feedback is a feasible strategy to study the human membrane receptor interactome.
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000008400 65320 $$2Author$$aData integration
000008400 65320 $$2Author$$aMembrane proteins
000008400 65320 $$2Author$$aProtein-protein interaction network
000008400 65320 $$2Author$$aReceptor interactome
000008400 65320 $$2Author$$aReceptor crosstalk
000008400 65320 $$2Author$$aSignal transcluction
000008400 65320 $$2Author$$aSystems biology
000008400 650_2 $$2MeSH$$aComputational Biology: methods
000008400 650_2 $$2MeSH$$aHumans
000008400 650_2 $$2MeSH$$aProtein Interaction Mapping: methods
000008400 650_2 $$2MeSH$$aProteome: analysis
000008400 650_2 $$2MeSH$$aProteome: metabolism
000008400 650_2 $$2MeSH$$aProteomics: methods
000008400 650_2 $$2MeSH$$aReceptor, Epidermal Growth Factor: analysis
000008400 650_2 $$2MeSH$$aReceptor, Epidermal Growth Factor: metabolism
000008400 650_2 $$2MeSH$$aReceptors, Cell Surface: analysis
000008400 650_2 $$2MeSH$$aReceptors, Cell Surface: metabolism
000008400 650_2 $$2MeSH$$aSignal Transduction
000008400 650_2 $$2MeSH$$aSystems Biology: methods
000008400 650_7 $$00$$2NLM Chemicals$$aProteome
000008400 650_7 $$00$$2NLM Chemicals$$aReceptors, Cell Surface
000008400 650_7 $$0EC 2.7.10.1$$2NLM Chemicals$$aEGFR protein, human
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000008400 7001_ $$0P:(DE-HGF)0$$aDhiman, H.K.$$b1
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000008400 7001_ $$0P:(DE-HGF)0$$aBudyak, I.$$b3
000008400 7001_ $$0P:(DE-HGF)0$$aKar, S.$$b4
000008400 7001_ $$0P:(DE-HGF)0$$aMan, D.$$b5
000008400 7001_ $$0P:(DE-HGF)0$$aDutta, A.$$b6
000008400 7001_ $$0P:(DE-HGF)0$$aTirupula, K.$$b7
000008400 7001_ $$0P:(DE-HGF)0$$aCarr, B.$$b8
000008400 7001_ $$0P:(DE-HGF)0$$aGrandis, J.R.$$b9
000008400 7001_ $$0P:(DE-HGF)0$$aBar-Joseph, Z.$$b10
000008400 7001_ $$0P:(DE-Juel1)VDB44599$$aKlein-Seetharaman, J.$$b11$$uFZJ
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