Dimension reduction with redundant gene elimination for tumor classification
Abstract
Background Analysis of gene expression data for tumor classification is an important application of bioinformatics methods. But it is hard to analyse gene expression data from DNA microarray experiments by commonly used classifiers, because there are only a few observations but with thousands of measured genes in the data set. Dimension reduction is often used to handle such a high dimensional problem, but it is obscured by the existence of...
Paper Details
Title
Dimension reduction with redundant gene elimination for tumor classification
Published Date
May 1, 2008
Journal
Volume
9
Issue
S6
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