A novel identified temporal protein complexes strategy inspired by density-distance and brainstorming process

Abstract

Detection of protein complexes and functional modules plays a crucial role for strengthening the comprehension of cellular organization and biological functions on the dynamic protein-protein interaction network. In this article, we put forward a new strategy to identify temporal protein complexes. Integrating time-course gene expression data into static protein interaction data, a series of time-sequenced subnetworks were constructed. Then we combined the network topology and gene ontology information for defining the distance between proteins in PPI network. A novel method to find the cluster centers and then form initial clusters was based on the idea that cluster centers are usually recognized as nodes with higher densities than their neighbors and with a relatively larger distance from other cluster centers. Finally, inspired by the brainstorming discussion process, two ways are introduced to update the initial clusters for achieving the optimal results. After the filtering and merging procedure, experimental results demonstrated that the proposed strategy had a good performance comparing with the other four advanced algorithms - MCODE, FAG-EC, HC-PIN, and CNC.