You are in:Home/Publications/Banu PKN, Azar AT, Inbarani HH (2017). Fuzzy firefly clustering for tumor and cancer analysis. International Journal of Modelling, Identification and Control (IJMIC), 27(2): 92-103. [SCOPUS CiteScore 2016: 2.38].

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Banu PKN, Azar AT, Inbarani HH (2017). Fuzzy firefly clustering for tumor and cancer analysis. International Journal of Modelling, Identification and Control (IJMIC), 27(2): 92-103. [SCOPUS CiteScore 2016: 2.38].
Authors: Not Available
Year: 2017
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
Journal: International Journal of Modelling, Identification and Control
Volume: 27
Issue: 2
Pages: 92-103
Publisher: Inderscience Enterprises Ltd.
Local/International: International
Paper Link:
Full paper Not Available
Supplementary materials Not Available
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.

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