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Predicting protein functions from redundancies in large-scale protein interaction networksInterpreting 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 method is not overly sensitive to the false positives present in the data. Even after adding 50% randomly generated interactions to the measured data set, we are able to recover almost all (approximately 89%) of the original associations.
Document ID
20040087488
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Samanta, Manoj Pratim
(NASA Ames Research Center Moffett Field CA United States)
Liang, Shoudan
Date Acquired
August 21, 2013
Publication Date
October 28, 2003
Publication Information
Publication: Proceedings of the National Academy of Sciences of the United States of America
Volume: 100
Issue: 22
ISSN: 0027-8424
Subject Category
Life Sciences (General)
Distribution Limits
Public
Copyright
Other

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