A Bayesian networks approach for predicting protein-protein interactions from genomic data.

by: R Jansen, H Yu, D Greenbaum, Y Kluger, NJ Krogan, S Chung, A Emili, M Snyder, JF Greenblatt, M Gerstein

Science, Vol. 302, No. 5644. (17 October 2003), pp. 449-453.

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Abstract

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.

@article{citeulike:306145, abstract = {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.}, address = {Department of Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Avenue, Post Office Box 208114, New Haven, CT 06520, USA.}, author = {Jansen, R. and Yu, H. and Greenbaum, D. and Kluger, Y. and Krogan, N. J. and Chung, S. and Emili, A. and Snyder, M. and Greenblatt, J. F. and Gerstein, M. }, citeulike-article-id = {306145}, doi = {http://dx.doi.org/10.1126/science.1087361}, issn = {1095-9203}, journal = {Science}, month = {October}, number = {5644}, pages = {449--453}, posted-at = {2007-11-30 23:00:20}, priority = {3}, title = {A Bayesian networks approach for predicting protein-protein interactions from genomic data.}, url = {http://dx.doi.org/10.1126/science.1087361}, volume = {302}, year = {2003} }

RIS record

TY - JOUR ID - citeulike:306145 L3 - citeulike-article-id:306145 TI - A Bayesian networks approach for predicting protein-protein interactions from genomic data. JF - Science VL - 302 IS - 5644 SP - 449 EP - 453 AD - Department of Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Avenue, Post Office Box 208114, New Haven, CT 06520, USA. SN - 1095-9203 N2 - 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. AU - Jansen, R AU - Yu, H AU - Greenbaum, D AU - Kluger, Y AU - Krogan, NJ AU - Chung, S AU - Emili, A AU - Snyder, M AU - Greenblatt, JF AU - Gerstein, M PY - 2003/10/17/ UR - http://dx.doi.org/10.1126/science.1087361 ER -

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