Exploring Microarray Data with Correspondence Analysis

Stanislav Busygin () and Panos M. Pardalos ()
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Stanislav Busygin: University of Florida
Panos M. Pardalos: University of Florida

A chapter in Data Mining in Biomedicine, 2007, pp 25-37 from Springer

Abstract: Abstract Due to the rapid development of DNA microarray chips it has become possible to discover and predict genetic patterns relevant for various diseases on the basis of exploration of massive data sets provided by DNA microarray probes. A number of data mining techniques have been used for such exploration to achieve the desirable results. However, high dimensionality and uncertain accuracy of microarray datasets remain the major obstacles in revealing the most crucial genetic factors determining a particular disease. This chapter describes a microarray data processing technique based on the correspondence analysis that helps to handle this issue.

Keywords: Acute Myeloid Leukemia; Acute Lymphoblastic Leukemia; Singular Value Decomposition; Correspondence Analysis; Data Mining Technique (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/978-0-387-69319-4_2

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