Authors: P.K. Nizar Banu; Ahmad Taher Azar; H. Hannah Inbarani
Addresses: Department of Computer Applications, B.S. Abdur Rahman University, Chennai, India ' Faculty of Computers and Information, Benha University, Egypt; Nanoelectronics Integrated Systems Center (NISC), Nile University, Egypt ' Department of Computer Science, Periyar University, Salem, India
Abstract: Swarm intelligence represents a meta-heuristic approach to solve a wide variety of problems. Searching for similar patterns of genes is becoming very essential to predict the expression of genes under various conditions. Firefly clustering inspired by the behaviour of fireflies helps in grouping genes that behave alike. Contrasting hard clustering methodology, fuzzy clustering assigns membership values for every gene and predicts the possibility of belonging to every cluster. To distinguish highly expressed and suppressed genes, the research in this paper proposes an efficient fuzzy-firefly clustering by integrating the merits of firefly and fuzzy clustering. The proposed method is compared with other swarm optimisation based clustering algorithms. It is applied on five gene expression datasets. The clusters resulting from the proposed algorithm provide interpretations of different gene expression patterns present in the cancer datasets. Experimental results show the excellent performance of fuzzy-firefly clustering to separate co-expressed and co-regulated genes.
Keywords: fuzzy firefly algorithm; fuzzy clustering; metaheuristics; gene expression data; particle swarm optimisation; PSO; fuzzy PSO; fuzzy logic; swarm intelligence; tumour prediction; cancer prediction; cancerous genes; tumours.
International Journal of Modelling, Identification and Control, 2017 Vol.27 No.2, pp.92 - 103
Received: 30 Nov 2015
Accepted: 11 Jan 2016
Published online: 16 Mar 2017 *