An integrative measure of graph- and vector-based semantic similarity using information content distance

Gene Ontology (GO) and its annotation data have been widely used for genomic and proteomic analysis. In the past few years, various semantic similarity measures using GO have been proposed to quantify functional similarity between two proteins and assess validity of protein-protein interactions (PPIs). They are categorized as pairwise and groupwise approaches according to the strategies of deriving protein-to-protein functional similarity. We propose a novel semantic similarity measure, called simVICD, which is a graph-and vector-based groupwise approach. This method computes the magnitude of a common induced subgraph as semantic similarity between two sets of terms annotating two proteins, respectively. The magnitude of the common induced subgraph is represented as the Euclidean norm of a vector having information content distance of all possible directed shortest paths in the induced subgraph. Our experimental results show that the proposed groupwise approach, simVICD, and a previous integrative pairwise approach, simICND, outperform the other existing semantic similarity methods in predicting protein complexes and identifying essential proteins.