A Bayesian networks approach for predicting protein-protein interactions from genomic data. Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N. J., Chung, S., Emili, A., Snyder, M., Greenblatt, J. F., & Gerstein, M. Science (New York, N.Y.), 302(5644):449--453, October, 2003. doi abstract bibtex 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{jansen_bayesian_2003,
title = {A {Bayesian} networks approach for predicting protein-protein interactions from genomic data},
volume = {302},
issn = {1095-9203},
doi = {10.1126/science.1087361},
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.},
language = {eng},
number = {5644},
journal = {Science (New York, N.Y.)},
author = {Jansen, Ronald and Yu, Haiyuan and Greenbaum, Dov and Kluger, Yuval and Krogan, Nevan J. and Chung, Sambath and Emili, Andrew and Snyder, Michael and Greenblatt, Jack F. and Gerstein, Mark},
month = oct,
year = {2003},
pmid = {14564010},
keywords = {Bayes Theorem, DEAD-box RNA Helicases, DNA Replication, Gene Expression, Genome, Fungal, Likelihood Functions, Nucleosomes, Peptide Chain Elongation, Translational, Protein Interaction Mapping, Proteomics, RNA Helicases, RNA, Messenger, RNA-Binding Proteins, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Sensitivity and Specificity},
pages = {449--453}
}
Downloads: 0
{"_id":"QADpfWkTYCBwnRihY","bibbaseid":"jansen-yu-greenbaum-kluger-krogan-chung-emili-snyder-etal-abayesiannetworksapproachforpredictingproteinproteininteractionsfromgenomicdata-2003","downloads":0,"creationDate":"2017-08-31T17:02:20.876Z","title":"A Bayesian networks approach for predicting protein-protein interactions from genomic data","author_short":["Jansen, R.","Yu, H.","Greenbaum, D.","Kluger, Y.","Krogan, N. J.","Chung, S.","Emili, A.","Snyder, M.","Greenblatt, J. F.","Gerstein, M."],"year":2003,"bibtype":"article","biburl":"http://bibbase.org/zotero/jordanashworth","bibdata":{"bibtype":"article","type":"article","title":"A Bayesian networks approach for predicting protein-protein interactions from genomic data","volume":"302","issn":"1095-9203","doi":"10.1126/science.1087361","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.","language":"eng","number":"5644","journal":"Science (New York, N.Y.)","author":[{"propositions":[],"lastnames":["Jansen"],"firstnames":["Ronald"],"suffixes":[]},{"propositions":[],"lastnames":["Yu"],"firstnames":["Haiyuan"],"suffixes":[]},{"propositions":[],"lastnames":["Greenbaum"],"firstnames":["Dov"],"suffixes":[]},{"propositions":[],"lastnames":["Kluger"],"firstnames":["Yuval"],"suffixes":[]},{"propositions":[],"lastnames":["Krogan"],"firstnames":["Nevan","J."],"suffixes":[]},{"propositions":[],"lastnames":["Chung"],"firstnames":["Sambath"],"suffixes":[]},{"propositions":[],"lastnames":["Emili"],"firstnames":["Andrew"],"suffixes":[]},{"propositions":[],"lastnames":["Snyder"],"firstnames":["Michael"],"suffixes":[]},{"propositions":[],"lastnames":["Greenblatt"],"firstnames":["Jack","F."],"suffixes":[]},{"propositions":[],"lastnames":["Gerstein"],"firstnames":["Mark"],"suffixes":[]}],"month":"October","year":"2003","pmid":"14564010","keywords":"Bayes Theorem, DEAD-box RNA Helicases, DNA Replication, Gene Expression, Genome, Fungal, Likelihood Functions, Nucleosomes, Peptide Chain Elongation, Translational, Protein Interaction Mapping, Proteomics, RNA Helicases, RNA, Messenger, RNA-Binding Proteins, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Sensitivity and Specificity","pages":"449--453","bibtex":"@article{jansen_bayesian_2003,\n\ttitle = {A {Bayesian} networks approach for predicting protein-protein interactions from genomic data},\n\tvolume = {302},\n\tissn = {1095-9203},\n\tdoi = {10.1126/science.1087361},\n\tabstract = {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.},\n\tlanguage = {eng},\n\tnumber = {5644},\n\tjournal = {Science (New York, N.Y.)},\n\tauthor = {Jansen, Ronald and Yu, Haiyuan and Greenbaum, Dov and Kluger, Yuval and Krogan, Nevan J. and Chung, Sambath and Emili, Andrew and Snyder, Michael and Greenblatt, Jack F. and Gerstein, Mark},\n\tmonth = oct,\n\tyear = {2003},\n\tpmid = {14564010},\n\tkeywords = {Bayes Theorem, DEAD-box RNA Helicases, DNA Replication, Gene Expression, Genome, Fungal, Likelihood Functions, Nucleosomes, Peptide Chain Elongation, Translational, Protein Interaction Mapping, Proteomics, RNA Helicases, RNA, Messenger, RNA-Binding Proteins, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Sensitivity and Specificity},\n\tpages = {449--453}\n}\n\n","author_short":["Jansen, R.","Yu, H.","Greenbaum, D.","Kluger, Y.","Krogan, N. J.","Chung, S.","Emili, A.","Snyder, M.","Greenblatt, J. F.","Gerstein, M."],"key":"jansen_bayesian_2003","id":"jansen_bayesian_2003","bibbaseid":"jansen-yu-greenbaum-kluger-krogan-chung-emili-snyder-etal-abayesiannetworksapproachforpredictingproteinproteininteractionsfromgenomicdata-2003","role":"author","urls":{},"keyword":["Bayes Theorem","DEAD-box RNA Helicases","DNA Replication","Gene Expression","Genome","Fungal","Likelihood Functions","Nucleosomes","Peptide Chain Elongation","Translational","Protein Interaction Mapping","Proteomics","RNA Helicases","RNA","Messenger","RNA-Binding Proteins","Saccharomyces cerevisiae","Saccharomyces cerevisiae Proteins","Sensitivity and Specificity"],"downloads":0},"search_terms":["bayesian","networks","approach","predicting","protein","protein","interactions","genomic","data","jansen","yu","greenbaum","kluger","krogan","chung","emili","snyder","greenblatt","gerstein"],"keywords":["bayes theorem","dead-box rna helicases","dna replication","gene expression","genome","fungal","likelihood functions","nucleosomes","peptide chain elongation","translational","protein interaction mapping","proteomics","rna helicases","rna","messenger","rna-binding proteins","saccharomyces cerevisiae","saccharomyces cerevisiae proteins","sensitivity and specificity"],"authorIDs":[],"dataSources":["BBpzLHanDZ6NX8vCB"]}