Title: Handling gene redundancy in microarray data using Grey Relational Analysis

Authors: Li-Juan Zhang, Zhou-Jun Li, Huo-Wang Chen

Addresses: National Laboratory for Parallel and Distributed Processing, Changsha 410073, China. ' School of Computer Science and Engineering, Beihang University, Beijing 100083, China. ' National Laboratory for Parallel and Distributed Processing, Changsha 410073, China

Abstract: Gene selection is one of the important and frequently used techniques for microarray data classification. In this paper, we introduce a new metric to measure gene-class relevance and gene-gene redundancy. The new metric is based on Grey Relational Analysis (GRA), called Grey Relational Grade (GRG), and never used in gene selection before. Based on the GRG, we develop a new gene selection method, which uses GRG to group similar genes to clusters, and then select informative genes from each cluster to avoid redundancy. Experiments on public data sets demonstrate the effectiveness of the proposed method.

Keywords: gene selection; grey relational analysis; microarray data classification; GRA; gene redundancy; data mining; bioinformatics; gene-class relevance; gene-gene redundancy; metrics; clustering.

DOI: 10.1504/IJDMB.2008.019094

International Journal of Data Mining and Bioinformatics, 2008 Vol.2 No.2, pp.134 - 144

Published online: 28 Jun 2008 *

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