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

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 .

Keywords

Bayesian probabilityComputational biologyComputer scienceSensitivity (control systems)GenomeProtein–protein interactionInteraction networkColocalizationData miningMachine learningArtificial intelligenceBiologyGeneticsGene

MeSH Terms

Bayes TheoremDEAD-box RNA HelicasesDNA ReplicationGene ExpressionGenomeFungalLikelihood FunctionsNucleosomesPeptide Chain ElongationTranslationalProtein Interaction MappingProteomicsRNA HelicasesRNAMessengerRNA-Binding ProteinsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsSensitivity and Specificity

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Publication Info

Year: 2003
Type: article
Volume: 302
Issue: 5644
Pages: 449-453
Citations: 1270
Access: Closed

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APA Style

                            
                                
                                    Ronald Jansen, 
                                
                                    Haiyuan Yu, 
                                
                                    Dov Greenbaum
                                
                                et al.
                            
                            (2003). 
                            A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data. 
                            Science
                            , 302
                            (5644)
                            , 449-453.
                            https://doi.org/10.1126/science.1087361
                        

Identifiers

DOI: 10.1126/science.1087361
PMID: 14564010

Data Quality

Data completeness: 86%