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Science 17 October 2003:
Vol. 302. no. 5644, pp. 449 - 453
DOI: 10.1126/science.1087361
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Reports

A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data

Ronald Jansen,1* Haiyuan Yu,1 Dov Greenbaum,1 Yuval Kluger,1 Nevan J. Krogan,4 Sambath Chung,1,2 Andrew Emili,4 Michael Snyder,2 Jack F. Greenblatt,4 Mark Gerstein1,3{dagger}

We have 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., messenger RNAcoexpression, coessentiality, and colocalization). In addition to de novo predictions, it can integrate often noisy, experimental interaction data sets. We observe that at given levels of sensitivity, our predictions are more accurate than the existing high-throughput experimental data sets. We validate our predictions with TAP (tandem affinity purification) tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint.

1 Department of Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Avenue, Post Office Box 208114, New Haven, CT 06520, USA.
2 Department of Molecular, Cellular and Developmental Biology, Yale University, 266 Whitney Avenue, Post Office Box 208114, New Haven, CT 06520, USA.
3 Department of Computer Science, Yale University, 266 Whitney Avenue, Post Office Box 208114, New Haven, CT 06520, USA.
4 Banting and Best Department of Medical Research, Department of Molecular and Medical Research, University of Toronto, Toronto, M5G 1L6, Ontario, Canada.

* Present address: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 307 West 63rd Street, New York, NY 10021, USA.

{dagger} To whom correspondence should be addressed. E-mail: mark.gerstein{at}yale.edu

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