Data Mining in Gene Expression Analysis: A Survey

Data Mining in Gene Expression Analysis: A Survey

Jilin Han (University of Oklahoma, USA), Le Gruenwald (University of Oklahoma, USA) and Tyrrell Conway (University of Oklahoma, USA)
DOI: 10.4018/978-1-59904-951-9.ch096
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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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