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

TLDR

The study develops a Bayesian network approach to predict genome‑wide protein–protein interactions in yeast. The method combines weakly associated genomic features such as mRNA co‑expression, co‑essentiality, and colocalization, integrates noisy experimental interaction data, and is validated by TAP tagging experiments. The predictions outperform existing high‑throughput experimental datasets in accuracy at comparable sensitivity, and the comprehensive interaction map is available at genecensus.org/intint.

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.