Current Bioinformatics

Yi-Ping Phoebe Chen
Department of Computer Science and Information Technology
La Trobe University
Melbourne
Australia

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On Biclustering of Gene Expression Data

Author(s): Anirban Mukhopadhyay, Ujjwal Maulik, Sanghamitra Bandyopadhyay.

Abstract:

Microarray technology enables the monitoring of the expression patterns of a huge number of genes across different experimental conditions or time points simultaneously. Biclustering of microarray data is an important technique to discover a group of genes that are co-regulated in a subset of experimental conditions. Traditional clustering algorithms find groups of genes/conditions over the complete feature space. Therefore they may fail to discover the local patterns where a subset of genes has similar behaviour over a subset of conditions. Biclustering algorithms aim to discover such local patterns from the gene expression matrix, thus can be thought as simultaneous clustering of genes and conditions. In recent years, a large number of biclustering algorithms have been proposed in literature. In this article, a study has been made on various issues regarding the biclustering problem along with a comprehensive survey on available biclustering algorithms. Moreover, a survey on freely available biclustering software is also made.

Keywords: Microarray, gene expression, biclustering, bicluster types, biclustering algorithms, biclustering software

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Article Details

VOLUME: 5
ISSUE: 3
Year: 2010
Page: [204 - 216]
Pages: 13
DOI: 10.2174/157489310792006701
Price: $58