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)


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.


Acute Myeloid Leukemia Acute Lymphoblastic Leukemia Gene Expression Data Logic Minimization Subgroup Discovery 
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  1. 1.
    Gamberger, D., Lavrač, N.: Expert-guided subgroup discovery: Methodology and application. Journal of Artficial Intelligence Research 17, 501–527 (2002)zbMATHGoogle Scholar
  2. 2.
    Golub, T.R., et al.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)CrossRefGoogle Scholar
  3. 3.
    Lavrač, N., Gamberger, D., Turney, P.: A relevancy filter for constructive induction. IEEE Intelligent Systems & Their Applications 13, 50–56 (1997)CrossRefGoogle Scholar
  4. 4.
    Li, J., Wong, L.: Geography of differences between two classes of data. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 325–337. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Ramaswamy, S., et al.: Multiclass cancer diagnosis using tumor gene expression signitures. Proc. Natl. Acad. Sci USA 98(26), 15149–15154 (2001)CrossRefGoogle Scholar

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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