Data Mining Methods for Microarray Data Analysis

Data Mining Methods for Microarray Data Analysis

Lei Yu (Arizona State University, USA) and Huan Liu (Arizona State University, USA)
Copyright: © 2005 |Pages: 5
DOI: 10.4018/978-1-59140-557-3.ch054
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Abstract

The advent of gene expression microarray technology enables the simultaneous measurement of expression levels for thousands or tens of thousands of genes in a single experiment (Schena, et al., 1995). Analysis of gene expression microarray data presents unprecedented opportunities and challenges for data mining in areas such as gene clustering (Eisen, et al., 1998; Tamayo, et al., 1999), sample clustering and class discovery (Alon, et al., 1999; Golub, et al., 1999), sample class prediction (Golub, et al., 1999; Wu, et al., 2003), and gene selection (Xing, Jordan, & Karp, 2001; Yu & Liu, 2004). This article introduces the basic concepts of gene expression microarray data and describes relevant data-mining tasks. It briefly reviews the state-of-the-art methods for each data-mining task and identifies emerging challenges and future research directions in microarray data analysis.

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