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| Content Provider | PubMed Central |
|---|---|
| Author | Samanta, Manoj Pratim Liang, Shoudan |
| Copyright Year | 2003 |
| 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 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 (≈89%) of the original associations. |
| Related Links | http://dx.doi.org/10.1073/pnas.2132527100 |
| Ending Page | 12583 |
| Page Count | 5 |
| Starting Page | 12579 |
| File Format | |
| ISSN | 00278424 |
| e-ISSN | 10916490 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Issue Number | 22 |
| Volume Number | 100 |
| Language | English |
| Publisher | National Academy of Sciences |
| Publisher Date | 2003-10-28 |
| Access Restriction | Open |
| Rights Holder | National Academy of Sciences |
| Subject Keyword | General Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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