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Analysis of Gene Expression Data by the Logic Minimization Approach

  • Dragan Gamberger
  • Nada Lavrač
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2780)

Abstract

This paper presents an application of machine learning algorithms based on inductive learning by logic minimization to the analysis of gene expression data. The characteristic properties of these data are a very large number of attributes (genes) and a relatively small number of examples (samples). Approaches to gene set reduction and to the detection of important disease markers are described. The results obtained on two well known publicly available gene expression classification problems are presented.

Keywords

Acute Myeloid Leukemia Acute Lymphoblastic Leukemia Gene Expression Data Logic Minimization Subgroup Discovery 
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 2003

Authors and Affiliations

  • Dragan Gamberger
    • 1
  • Nada Lavrač
    • 2
  1. 1.Rudjer Bošković InstituteZagrebCroatia
  2. 2.Jožef Stefan InstituteLjubljanaSlovenia

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