Accurate mapping of protein-protein interaction networks in model organisms is a crucial first step toward subsequent quantitative study of the organization and evolution of biological systems. Data quality of experimental interactome maps can be assessed and improved by integrating multiple sources of evidence using machine learning methods. Here we describe the commonly used algorithms for predicting protein-protein interaction by genome data integration, and discuss several important yet often overlooked issues in computational reconstruction of protein-protein interaction networks.