Integrating multiple biomedical resources for protein complex prediction

Prediction of protein complexes from protein-protein interaction (PPI) networks is crucial to unraveling the principles of cellular organization. Most existing approaches only exploit high-throughput experimental PPI data to predict protein complexes. In this paper, we integrate the multiple biomedical resources for protein complex prediction by constructing attributed PPI networks, which include high-throughput data, co-expression data, genomic data, text mining data and gene ontology data. Multiple biomedical resources are complementary in attributed PPI networks. We propose a novel approach called IMBP based on attributed PPI networks. IMBP can effectively learn the degree of contributions of different biomedical resource for complex prediction. The experimental results show that IMBP can make good use of multiple biomedical data and achieve state-of-the-art performance.