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Parsimonious selection of useful genes in microarray gene expression data

Author
González, F.F.; Belanche, Ll.
Type of activity
Book chapter
Book
Software tools and algorithms for biological systems
First page
45
Last page
55
Publisher
Springer
Date of publication
2011
ISBN
978-1-4419-7045-9 Open in new window
DOI
https://doi.org/10.1007/978-1-4419-7046-6_5 Open in new window
Repository
http://hdl.handle.net/2117/19482 Open in new window
Abstract
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 achieveme...
Citation
González, 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.
Keywords
Biological data mining and knowledge discovery, Cancer informatics, Gene expression analysis, Tools and methods for computational biology and bioinformatics
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

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