A graph-based cluster ensemble method to detect protein functional modules from multiple information sources

Abstract

Many works have been done to identify functional modules in Protein-Protein Interaction (PPI) networks but the results are far from satisfaction. One main reason is that the PPI data generated from high-throughput experiments is noisy and incomplete. Solving the problem goes beyond what a single data source can provide and thus requires the integration of multiple information sources. To address this problem, we hereby propose a graph-based cluster ensemble method which integrates gene ontology (GO) and gene expression data with PPI networks. Experimental results show that our method is superior to the baseline methods and demonstrate the benefits of integrating multiple biological information sources and diverse clustering methods.