Predicting protein functions from redundancies in large-scale protein interaction networks.

ABSTRACT: Interpreting data from large-scale protein interaction experiments has been a challenging task because of the widespread presence of random false positives. Here, we present a network-based statistical algorithm that overcomes this difficulty and allows us to derive functions of unannotated proteins from large-scale interaction data. Our algorithm uses the insight that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals >2,800 reliable functional associations, 29% of which involve at least one unannotated protein. By further analyzing these associations, we derive tentative functions for 81 unannotated proteins with high certainty. Our

SUBMITTER: Samanta MP 

PROVIDER: S-EPMC240660 | biostudies-literature | 2003 Oct

REPOSITORIES: biostudies-literature