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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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
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