Machine Learning methods have of late made significant efforts to solving multidisciplinary problems in the field of cancer classification in microarray gene expression data. These tasks are characterized by a large number of features and a few observations, making the modeling a non-trivial undertaking. In this work we apply entropic filter methods for gene selection, in combination with several off-the-shelf classifiers. The introduction of bootstrap resampling techniques permits the achievement of more stable performance estimates. Our findings show that the proposed methodology permits a drastic reduction in dimension, offering attractive solutions both in terms of prediction accuracy and number of explanatory genes; a dimensionality reduction technique preserving discrimination capabilities is used for visualization of the selected genes.
CitationGonzález, F.F.; Belanche, Ll. Parsimonious selection of useful genes in microarray gene expression data. A: "Software tools and algorithms for biological systems". Springer, 2011, p. 45-55.
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