Exploring Microarray Data with Correspondence Analysis

  • Stanislav Busygin
  • Panos M. Pardalos
Part of the Springer Optimization and Its Applications book series (SOIA, volume 7)


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.


Acute Myeloid Leukemia Acute Lymphoblastic Leukemia Singular Value Decomposition Correspondence Analysis Data Mining Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Stanislav Busygin
    • 1
  • Panos M. Pardalos
    • 1
  1. 1.Industrial and Systems Engineering DepartmentUniversity of FloridaGainesville

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