ACC–FMD: ant colony clustering for functional module detection in protein–protein interaction networks. (6th February 2015)

• Record Type: Journal Article
• Authors: Ji, Junzhong; Liu, Hongxin; Zhang, Aidong; Liu, Zhijun; Liu, Chunnian
• Is Part Of: International journal of data mining and bioinformatics. Volume 11:Number 3(2015)
• Page Start: 331
• Page End: 363
• Publication Date: 2015-02-06
• Subjects: protein–protein interaction -- PPI networks -- functional module detection -- ant colony optimisation -- ACO -- clustering algorithms -- picking model -- dropping model -- similarity function -- bioinformatics -- data mining; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.