Gene Clustering Using Metaheuristic Optimization Algorithms

P. K. Nizar Banu and S. Andrews
Additional contact information
P. K. Nizar Banu: Department of Computer Applications, B.S. Abdur Rahman University, Chennai, India
S. Andrews: Department of Information Technology, Mahendra Engineering College, Mallasamudram, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2015, vol. 6, issue 4, 14-38

Abstract: Gene clustering is a familiar step in the exploratory analysis of high dimensional biological data. It is the process of grouping genes of similar patterns in the same cluster and aims at analyzing the functions of gene that leads to the development of drugs and early diagnosis of diseases. In the recent years, much research has been proposed using nature inspired meta-heuristic algorithms. Cuckoo Search is one such optimization algorithm inspired from nature by breeding strategy of parasitic bird, the cuckoo. This paper proposes cuckoo search clustering and clustering using levy flight cuckoo search for grouping brain tumor gene expression dataset. A comparative study is made with genetic algorithm, PSO clustering, cuckoo search clustering and clustering using levy flight cuckoo search. Levy flight is an important property of levy distribution which covers the entire search space. Breeding pattern of cuckoo is associated with the genes that cause tumor to grow and affect other organs gradually. Clusters generated by these algorithms are validated to find the closeness among the genes in a cluster and separation of genes between clusters. Experimental results carried out in this paper show that cuckoo search clustering outperforms other clustering methods used for experimentation.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJAMC.2015100102 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jamc00:v:6:y:2015:i:4:p:14-38

Access Statistics for this article

International Journal of Applied Metaheuristic Computing (IJAMC) is currently edited by Peng-Yeng Yin

More articles in International Journal of Applied Metaheuristic Computing (IJAMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().