ACC-FMD: ant colony clustering for functional module detection in protein-protein interaction networks

Abstract

Mining functional modules in Protein-Protein Interaction (PPI) networks is a very important research for revealing the structure-functionality relationships in biological processes. More recently, some swarm intelligence algorithms have been successfully applied in the field. This paper presents a new nature-inspired approach, ACC-FMD, which is based on ant colony clustering to detect functional modules. First, some proteins with the higher clustering coefficients are, respectively, selected as ant seed nodes. And then, the picking and dropping operations based on ant probabilistic models are developed and employed to assign proteins into the corresponding clusters represented by seeds. Finally, the best clustering result in each generation is used to perform the information transmission by updating the similarly function. Experimental results on some benchmarked datasets show that ACC-FMD outperforms the CFinder and MCODE algorithms and has comparative performance with the MINE, COACH, DPClus and Core algorithms in terms of the general evaluation metrics.

Ant Colony Clustering Approach Combined with Multilevel Framework for Functional Module Detection in Large-Scale PPI Networks

“…As for the excellent results in small-scale PPI networks, the ant colony clustering algorithm [18], which is accomplished by picking up and dropping models, is used.…”

“…In coarsening phase, the coarsening level is n, so the time complexity of matching operation is O(|V 0 | * d 0max + |V 1 | * d 1max + · · · + |V n | * d nmax ), which can be simplified as O(|V 0 |). In the initial clustering phase, the time complexity is O(N * M * k 2 1 * |V n | 2 * d nmax ) [18] and it can be simplified as O(|V n | 2 ). In the refinement phase, the time complexity is O(1).…”

“…To reduce the runtime of our previous algorithm ACC-FMD introduced in [18] in large-scale PPI networks, we propose the ant colony clustering algorithm combined with a multilevel framework, ACC-MLF, to detect functional modules in PPI networks in this paper. The research goal is to use the global search ability of ant colony clustering and the reduced capacity of multilevel scheme to get clustering results effectively and efficiently.…”

Cardioprotection Effects of Sevoflurane by Regulating the Pathway of Neuroactive Ligand-Receptor Interaction in Patients Undergoing Coronary Artery Bypass Graft Surgery

“…Although several significant genes showing no difference might not be detected via their own behavior, they can be identified when these genes worked together with other genes (e.g., as modules). Accordingly, several studies have identified functional modules from PPI networks [20, 21]. …”