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A Novel Discretization Method for Microarray-Based Cancer Classification

  • Ding Li
  • Rui Li
  • Hong-Qiang Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

In this paper, we propose a gene expression diversity-based method for gene expression discretization. By counting the numbers of samples of different classes in an open expression intervals, the method calculates class distribution diversity and then expression diversity for genes. Based on the gene expression diversity, three discretization criteria are established for discretizing gene expression levels. We evaluate the proposed method on the publicly available leukemia dataset and compare it with several previous methods.

Keywords

gene expression gene expression diversity gene regulation discretization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ding Li
    • 1
    • 2
  • Rui Li
    • 1
    • 2
  • Hong-Qiang Wang
    • 2
  1. 1.Department of AutomationUniversity of Science and Technology of ChinaHefeiP.R. China
  2. 2.Intelligent Computation Lab, Hefei Institute of Intelligent MachinesChinese Academy of ScienceHefeiP.R. China

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