TY - JOUR
T1 - A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data
AU - Jansen, Ronald
AU - Yu, Haiyuan
AU - Greenbaum, Dov
AU - Kluger, Yuval
AU - Krogan, Nevan J.
AU - Chung, Sambath
AU - Emili, Andrew
AU - Snyder, Michael
AU - Greenblatt, Jack F.
AU - Gerstein, Mark
PY - 2003/10/17
Y1 - 2003/10/17
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 RNA coexpression, 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.
AB - 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 RNA coexpression, 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=0142052944&partnerID=8YFLogxK
U2 - 10.1126/science.1087361
DO - 10.1126/science.1087361
M3 - Article
C2 - 14564010
AN - SCOPUS:0142052944
SN - 0036-8075
VL - 302
SP - 449
EP - 453
JO - Science
JF - Science
IS - 5644
ER -