Title: Genetic algorithm based clustering for gene-gene interaction in episodic memory

Authors: Sudhakar Tripathi; Ravi Bhushan Mishra; Anand Kumar Sharma

Addresses: Department of Computer Science and Engineering, National Institute of Technology Patna, Patna Bihar, India 800005 ' Department of Computer Science and Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, Uttar Pradesh, India 221005 ' Department of Computer Science and Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, Uttar Pradesh, India 221005

Abstract: After the identification of several disease-associated polymorphisms by genome-wide association analysis, it is now clear that gene-gene interactions are fundamental mechanisms for the development of complex diseases. In this paper, we propose a genetic algorithm (GA) based clustering algorithm to identify groups of related genes in episodic memory. This clustering method required number of clusters and number of genes in each cluster and fitness function. In this paper, we have taken STRING 9.1 clustering method result on episodic memory. We have used “interaction between clusters” as a fitness function for the GA and have compared the result of GA based clustering algorithm with standard K-means, STRING 9.1 K-means, hierarchical and self-organising maps. We have evaluated the performance of all the above methods using Rand index, Jaccard index and Minkowski index. Our comparative study demonstrates that the proposed GA is close to hierarchical clustering method as far as the performance is concerned.

Keywords: clustering; gene-gene interaction; genetic algorithm; hierarchical; K-means; SOM; STRING 9.1.

DOI: 10.1504/IJBRA.2019.101208

International Journal of Bioinformatics Research and Applications, 2019 Vol.15 No.3, pp.254 - 271

Received: 16 Jul 2016
Accepted: 01 Jun 2017
Published online: 29 Jul 2019 *

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