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A Bayesian networks approach for predicting protein-protein interactions from genomic data (2003)
| Venue: | SCIENCE |
| Citations: | 294 - 11 self |
BibTeX
@ARTICLE{Jansen03abayesian,
author = {Ronald Jansen and Haiyuan Yu and Dov Greenbaum and Yuval Kluger and Nevan J Krogan and Sambath Chung and Andrew Emili and Michael Snyder and Jack F Greenblatt and Mark Gerstein},
title = { A Bayesian networks approach for predicting protein-protein interactions from genomic data},
journal = {SCIENCE},
year = {2003},
pages = {449--453}
}
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Abstract
We developed an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast. Our method naturally weights and combines into reliable predictions genomic features only weakly associated with interaction (e.g., mRNA co-expression, co-essentiality and co-localization). In addition to de novo predictions, it can integrate often noisy, experimental interaction datasets. We observe that at given levels of sensitivity our predictions are more accurate than the existing highthroughput experimental datasets. We validate our predictions with new TAP-tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint.
Keyphrases
bayesian network protein-protein interaction genomic data protein-protein interaction genome-wide novo prediction yeast interaction experimental interaction datasets comprehensive view org intint new tap-tagging experiment mrna co-expression reliable prediction genomic feature highthroughput experimental datasets
