Optimizing Flow-based Modularization by Iterative Centroid Search in Protein Interaction Networks

Abstract

The systematic analysis of protein-protein interactions is a fundamental step for understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks. However, current unreliable interaction data and complex connectivity of interaction networks have made it challenging. We propose a novel metric, called semantic interactivity, to measure the reliability of protein-protein interactions using gene ontology (GO) annotation data. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability to each edge as a weight. We present an iterative centroid search (ICES) algorithm for optimizing the flow-based modularization method and identifying functional modules in a weighted interaction network. It iteratively performs two procedures: centroid search and flow simulation. Our experimental results show that the accuracy of modules is enhanced during the iteration.