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Cluster Analysis of Gene Expression Data

Cluster Analysis of Gene Expression Data

Alan Wee-Chung Liew, Ngai-Fong Law, Hong Yan
Copyright: © 2009 |Pages: 8
ISBN13: 9781599048499|ISBN10: 1599048493|EISBN13: 9781599048505
DOI: 10.4018/978-1-59904-849-9.ch045
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MLA

Wee-Chung Liew, Alan, et al. "Cluster Analysis of Gene Expression Data." Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, et al., IGI Global, 2009, pp. 289-296. https://doi.org/10.4018/978-1-59904-849-9.ch045

APA

Wee-Chung Liew, A., Law, N., & Yan, H. (2009). Cluster Analysis of Gene Expression Data. In J. Rabuñal Dopico, J. Dorado, & A. Pazos (Eds.), Encyclopedia of Artificial Intelligence (pp. 289-296). IGI Global. https://doi.org/10.4018/978-1-59904-849-9.ch045

Chicago

Wee-Chung Liew, Alan, Ngai-Fong Law, and Hong Yan. "Cluster Analysis of Gene Expression Data." In Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, Julian Dorado, and Alejandro Pazos, 289-296. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-59904-849-9.ch045

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Abstract

Important insights into gene function can be gained by gene expression analysis. For example, some genes are turned on (expressed) or turned off (repressed) when there is a change in external conditions or stimuli. The expression of one gene is often regulated by the expression of other genes. A detail analysis of gene expression information will provide an understanding about the inter-networking of different genes and their functional roles. DNA microarray technology allows massively parallel, high throughput genome-wide profiling of gene expression in a single hybridization experiment [Lockhart & Winzeler, 2000]. It has been widely used in numerous studies over a broad range of biological disciplines, such as cancer classification (Armstrong et al., 2002), identification of genes relevant to a certain diagnosis or therapy (Muro et al., 2003), investigation of the mechanism of drug action and cancer prognosis (Kim et al., 2000; Duggan et al., 1999). Due to the large number of genes involved in microarray experiment study and the complexity of biological networks, clustering is an important exploratory technique for gene expression data analysis. In this article, we present a succinct review of some of our work in cluster analysis of gene expression data.

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