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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Gamberger, D., & Lavrač, N. (2003). Analysis of gene expression data by the logic minimization approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2780 LNAI, pp. 244–248). https://doi.org/10.1007/978-3-540-39907-0_34
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