Title: MOACO Biclustering of gene expression data

Authors: Junwan Liu, Zhoujun Li, Xiaohua Hu, Yiming Chen

Addresses: School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410073, PR China. ' School of Computer Science and Engineering, Beihang University, Beijing 100191, PR China. ' College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, USA. ' School of Computer, National University of Deference Technology, Changsha 410073, PR China

Abstract: Many bioinformatics data sets come from DNA microarray experiments. Biclustering of gene expression data can identify genes with similar behaviour with respect to different conditions. Ant Colony Optimisation (ACO) algorithms have been shown to be effective problem solving strategies for a wide range of problem domains. Multiple Objective Ant Colony Optimisation (MOACO) mainly focuses on solving the multiple objective combinatorial optimisation problems. This paper incorporates crowding update technology into MOACOB and proposes crowding MOACO biclustering algorithm to mine biclusters from gene expression data. Experimental results are shown for biclustering algorithm on two real gene expression data.

Keywords: bioinformatics; DNA microarray; biclustering; gene expression data; ACO; ant colony optimisation; multiple objectives; crowding update technology.

DOI: 10.1504/IJFIPM.2010.033246

International Journal of Functional Informatics and Personalised Medicine, 2010 Vol.3 No.1, pp.58 - 72

Received: 01 Mar 2010
Accepted: 23 Mar 2010
Published online: 14 May 2010 *

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