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

Science. 2003 Oct 17;302(5644):449-53. doi: 10.1126/science.1087361.


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.

Publication types

  • Evaluation Study

MeSH terms

  • Bayes Theorem*
  • DEAD-box RNA Helicases
  • DNA Replication
  • Gene Expression
  • Genome, Fungal*
  • Likelihood Functions
  • Nucleosomes / metabolism
  • Peptide Chain Elongation, Translational
  • Protein Interaction Mapping*
  • Proteomics
  • RNA Helicases / metabolism
  • RNA, Messenger / genetics
  • RNA, Messenger / metabolism
  • RNA-Binding Proteins / metabolism
  • Saccharomyces cerevisiae / genetics
  • Saccharomyces cerevisiae / metabolism*
  • Saccharomyces cerevisiae Proteins / genetics
  • Saccharomyces cerevisiae Proteins / metabolism*
  • Sensitivity and Specificity


  • Nucleosomes
  • RNA, Messenger
  • RNA-Binding Proteins
  • Saccharomyces cerevisiae Proteins
  • DBP3 protein, S cerevisiae
  • DEAD-box RNA Helicases
  • RNA Helicases