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Data Mining in Gene Expression Analysis: A Survey

Data Mining in Gene Expression Analysis: A Survey

Jilin Han, Le Gruenwald, Tyrrell Conway
Copyright: © 2006 |Pages: 44
ISBN13: 9781591406556|ISBN10: 1591406552|EISBN13: 9781591406570
DOI: 10.4018/978-1-59140-655-6.ch013
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MLA

Han, Jilin, et al. "Data Mining in Gene Expression Analysis: A Survey." Processing and Managing Complex Data for Decision Support, edited by Jérôme Darmont and Omar Boussaid, IGI Global, 2006, pp. 375-418. https://doi.org/10.4018/978-1-59140-655-6.ch013

APA

Han, J., Gruenwald, L., & Conway, T. (2006). Data Mining in Gene Expression Analysis: A Survey. In J. Darmont & O. Boussaid (Eds.), Processing and Managing Complex Data for Decision Support (pp. 375-418). IGI Global. https://doi.org/10.4018/978-1-59140-655-6.ch013

Chicago

Han, Jilin, Le Gruenwald, and Tyrrell Conway. "Data Mining in Gene Expression Analysis: A Survey." In Processing and Managing Complex Data for Decision Support, edited by Jérôme Darmont and Omar Boussaid, 375-418. Hershey, PA: IGI Global, 2006. https://doi.org/10.4018/978-1-59140-655-6.ch013

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Abstract

The study of gene expression levels under defined experimental conditions is an important approach to understand how a living cell works. High-throughput microarray technology is a very powerful tool for simultaneously studying thousands of genes in a single experiment. This revolutionary technology results in an extensive amount of data, which raises an important question: how to extract meaningful biological information from these data? In this chapter, we survey data mining techniques that have been used for clustering, classification and association rules for gene expression data analysis. In addition, we provide a comprehensive list of currently available commercial and academic data mining software together with their features. Lastly, we suggest future research directions.

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