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Unsupervised Dense Regions Discovery in DNA Microarray Data

  • Andy M. Yip
  • Edmond H. Wu
  • Michael K. Ng
  • Tony F. Chan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3177)

Abstract

In this paper, we introduce the notion of dense regions in DNA microarray data and present algorithms for discovering them. We demonstrate that dense regions are of statistical and biological significance through experiments. A dataset containing gene expression levels of 23 primate brain samples is employed to test our algorithms. Subsets of potential genes distinguishing between species and a subset of samples with potential abnormalities are identified.

Keywords

Association Rule Dense Region Frequent Itemset Subspace Cluster Expression Index 
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-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Andy M. Yip
    • 1
  • Edmond H. Wu
    • 2
  • Michael K. Ng
    • 2
  • Tony F. Chan
    • 1
  1. 1.Department of MathematicsUniversity of CaliforniaLos AngelesUSA
  2. 2.Department of MathematicsThe University of Hong KongHong Kong

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