Identifying Protein Complexes Method Based on Time-Sequenced Association and Ant Colony Clustering in Dynamic PPI Networks

Abstract

As protein-protein interactions always change with time, environments and different stages of cell cycle, the clustering analysis on static protein-protein interaction (PPI) networks can not reflect this dynamics property and is far from satisfactory. To solve it, this paper proposes a method based on time-sequenced association and Ant Colony Clustering for identifying Protein Complexes in Dynamic PPI networks (called ACC-DPC). ACC-DPC first splits a PPI network into a series of dynamics subnetworks under different time points by integrating gene expression data, and then makes the clustering analysis on each subnetwork using the ant colony clustering method. For each subnetwork, ACC-DPC begins with constructing initial protein clusters by introducing the time-sequenced association characteristic of protein complexes between two adjacent time points, and later uses the picking up and dropping down operators of ant colony clustering to accomplish the clustering process of other proteins. The experimental results on two PPI datasets demonstrate that ACC-DPC has competitive performances in identifying protein complexes of dynamic PPI networks compared with several algorithms.