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Analysis of Gene Expression Data by the Logic Minimization Approach

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Artificial Intelligence in Medicine (AIME 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2780))

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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.

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References

  1. Gamberger, D., Lavrač, N.: Expert-guided subgroup discovery: Methodology and application. Journal of Artficial Intelligence Research 17, 501–527 (2002)

    MATH  Google Scholar 

  2. Golub, T.R., et al.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)

    Article  Google Scholar 

  3. Lavrač, N., Gamberger, D., Turney, P.: A relevancy filter for constructive induction. IEEE Intelligent Systems & Their Applications 13, 50–56 (1997)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  5. Ramaswamy, S., et al.: Multiclass cancer diagnosis using tumor gene expression signitures. Proc. Natl. Acad. Sci USA 98(26), 15149–15154 (2001)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Rudjer Bošković Institute, Bijenička 54, 10000, Zagreb, Croatia

    Dragan Gamberger

  2. Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Nada Lavrač

Authors
  1. Dragan Gamberger
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  2. Nada Lavrač
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Editor information

Editors and Affiliations

  1. INSERM U836-UJF-CEA-CHU (Grenoble Institute of Neuroscience),  

    Michel Dojat

  2. Department of Computer Science, University of Cyprus, P.O.Box 20537, CY-1678, Nicosia, Cyprus

    Elpida T. Keravnou

  3. Centro de Inteligência Artificial, Departamento de Informática, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal

    Pedro Barahona

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© 2003 Springer-Verlag Berlin Heidelberg

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Gamberger, D., Lavrač, N. (2003). Analysis of Gene Expression Data by the Logic Minimization Approach. In: Dojat, M., Keravnou, E.T., Barahona, P. (eds) Artificial Intelligence in Medicine. AIME 2003. Lecture Notes in Computer Science(), vol 2780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39907-0_34

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  • DOI: https://doi.org/10.1007/978-3-540-39907-0_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20129-8

  • Online ISBN: 978-3-540-39907-0

  • eBook Packages: Springer Book Archive

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