Dimension reduction with gene expression data using targeted variable importance measurement.

ABSTRACT:

Background

When a large number of candidate variables are present, a dimension reduction procedure is usually conducted to reduce the variable space before the subsequent analysis is carried out. The goal of dimension reduction is to find a list of candidate genes with a more operable length ideally including all the relevant genes. Leaving many uninformative genes in the analysis can lead to biased estimates and reduced power. Therefore, dimension reduction is often considered a necessary predecessor of the analysis because it can not only reduce the cost of handling numerous variables, but also has the potential to improve the performance of the downstream analysis algorithms.

Results

We propose a TMLE-VIM dimension reduction procedure based on the variable importance meas

SUBMITTER: Wang H 

PROVIDER: S-EPMC3166941 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature