2011
DOI: 10.1074/mcp.m111.010629
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Large-scale De Novo Prediction of Physical Protein-Protein Association

Abstract: Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future.Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested… Show more

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Cited by 49 publications
(58 citation statements)
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“…proteins (fewer than five known interactions). We also used these data sets to evaluate four other prediction methods 11,12,15,16 . We determined the overlaps between top predictions of each method, excluding any PPIs used in training, and these six reference sets (Fig.…”
Section: Validation Of Ppi Predictions By Multiple Approachesmentioning
confidence: 99%
“…proteins (fewer than five known interactions). We also used these data sets to evaluate four other prediction methods 11,12,15,16 . We determined the overlaps between top predictions of each method, excluding any PPIs used in training, and these six reference sets (Fig.…”
Section: Validation Of Ppi Predictions By Multiple Approachesmentioning
confidence: 99%
“…Nevertheless, experimentally confirmed PPIs stemming from two-hybrid system, AP-MS and small-scale interaction studies account for less than 25% of all human PPIs predicted by certain sources [83]. This gap in knowledge has motivated the development of innovative computational procedures for de novo prediction of PPIs, which are not based on direct experimental evidence.…”
Section: Bioinformatics From Global Proteomic and Genomic Datamentioning
confidence: 99%
“…In general, combining weak evidence for membership in an interactome can result in more successful predictors. Such approaches include Bayesian classifiers (18, 81, 112, 118, 194, 195), decision tree approaches (169) support vector machines (SVMs) (34, 101, 140), random forest classifiers (26, 27, 45), and a host of other techniques (13, 20, 74, 122, 126, 187). …”
Section: Building Interactomesmentioning
confidence: 99%