M-Finder: Functional association mining from protein interaction networks weighted by semantic similarity

Protein-protein interactions (PPIs) play a key role in understanding functional behavior of genes. Discovering association patterns from PPI networks is crucial for functional characterization on a system level. We present a novel approach to discover the functional association pattern of a query gene from the genome-wide PPI networks. This approach consists of two major components. First, we transform the PPI network to a weighted graph representation by measuring semantic similarity. Three enhanced semantic similarity methods are proposed to estimate functional closeness of each interacting pair. Second, we apply a dynamic propagation algorithm to detect the functional association pattern of a gene, represented as a sub-network. The size of the sub-networks is flexibly determined by user-specific parameters. In this paper, we also introduce an interactive web application, called M-Finder, to visualize the functional association pattern of a gene entered by a user. The semantic similarity measures and the dynamic propagation algorithm are embedded in this tool to run on up-to-date PPI networks of model species. M-Finder allows users to carry out further systematic analysis for functional characterization on the genomic scale.