| Autor: |
González-Navarro FF; Departament de Llenguatges i Sistemes Informatics, Universitat Politecnica de Catalunya, Omega Building, North Campus, Barcelona, Spain. belanche@lsi.upc.edu, Belanche-Muñoz LA |
| Jazyk: |
angličtina |
| Zdroj: |
Advances in experimental medicine and biology [Adv Exp Med Biol] 2011; Vol. 696, pp. 45-55. |
| DOI: |
10.1007/978-1-4419-7046-6_5 |
| Abstrakt: |
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 nontrivial undertaking. In this study, 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 in terms of both prediction accuracy and number of explanatory genes; a dimensionality reduction technique preserving discrimination capabilities is used for visualization of the selected genes. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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