Microarray Data Mining: Issues and Prospects

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Author(s): Giulia Bruno (Politecnico di Torino, Italy)and Alessandro Fiori (Politecnico di Torino, Italy)
Copyright: 2011
Pages: 25
Source title: Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains
Source Author(s)/Editor(s): A.V. Senthil Kumar (CMS College of Science and Commerce, India)
DOI: 10.4018/978-1-60960-067-9.ch002

Keywords: Data Mining and Databases / Information Science Reference / Knowledge Discovery / Library & Information Science

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Abstract

Microarray technology is a powerful tool to analyze thousands of gene expression values with a single experiment. Due to the huge amount of data, most of recent studies are focused on the analysis and the extraction of useful and interesting information from microarray data. Examples of applications include detecting genes highly correlated to diseases, selecting genes which show a similar behavior under specific conditions, building models to predict the disease outcome based on genetic profiles, and inferring regulatory networks. This chapter presents a review of four popular data mining techniques (i.e., Classification, Feature Selection, Clustering and Association Rule Mining) applied to microarray data. It describes the main characteristics of microarray data in order to understand the critical issues which are introduced by gene expression values analysis. Each technique is analyzed and examples of pertinent literature are reported. Finally, prospects of data mining research on microarray data are provided.

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