Title: Improved differential evolution for microarray analysis

Authors: Indrajit Saha; Dariusz Plewczynski; Ujjwal Maulik; Sanghamitra Bandyopadhyay

Addresses: Interdisciplinary Centre for Mathematical and Computational Modeling (ICM), University of Warsaw, Al. Żwirki i Wigury 93, ?oor 2 1/2, 02-089 Warsaw, Poland. ' Interdisciplinary Centre for Mathematical and Computational Modeling (ICM), University of Warsaw, Al. Żwirki i Wigury 93, ?oor 2 1/2, 02-089 Warsaw, Poland. ' Department of Computer Science and Engineering, Jadavpur University, Kolkata 700 032, West Bengal, India. ' Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, West Bengal, India

Abstract: Clustering is an important tool for analysing the microarray data to identify groups of co-expressed genes. The problem of fuzzy clustering in microarray data motivated us to develop an improved clustering algorithm. In this paper, an improved differential evolution based fuzzy clustering technique is proposed. The performance of the proposed improved differential evolution based fuzzy clustering technique has been compared with other state-of-the-art clustering algorithms for publicly available benchmark microarray data sets. Statistical and biological significance tests have been carried out to establish the statistical superiority of the proposed clustering approach and biological relevance of clusters of co-expressed genes, respectively.

Keywords: co-expressed genes; fuzzy clustering; improved differential evolution; biological significance test; microarray data analysis; bioinformatics.

DOI: 10.1504/IJDMB.2012.045542

International Journal of Data Mining and Bioinformatics, 2012 Vol.6 No.1, pp.86 - 103

Received: 20 Feb 2010
Accepted: 26 Aug 2010

Published online: 17 Dec 2014 *

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