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BIOINFORMATICS Vol. 19 no. 0 2003, pages 18
 

Summary: BIOINFORMATICS
Vol. 19 no. 0 2003, pages 1­8
DOI: 10.1093/bioinformatics/btg062
Effective dimension reduction methods for tumor
classification using gene expression data
A. Antoniadis , S. Lambert-Lacroix and F. Leblanc
Laboratoire IMAG-LMC, University Joseph Fourier, BP 53, 38041 Grenoble Cedex 9,
France
Received on April 21, 2002; revised on October 8, 2002; accepted on November 10, 2002
ABSTRACT
Motivation: One particular application of microarray data,
is to uncover the molecular variation among cancers. One
feature of microarray studies is the fact that the number
n of samples collected is relatively small compared to
the number p of genes per sample which are usually
in the thousands. In statistical terms this very large
number of predictors compared to a small number of
samples or observations makes the classification problem
difficult. An efficient way to solve this problem is by using
dimension reduction statistical techniques in conjunction

  

Source: Antoniadis, Anestis - Laboratoire Jean Kuntzmann, Université Joseph Fourier
Lambert, Sophie.- TIMC-IMAG Laboratoire techniques de l'imagerie, de la modélisation et de la cognition

 

Collections: Biology and Medicine; Mathematics