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