Maximal conditional chi-square importance in random forests.

ABSTRACT:

Motivation

High-dimensional data are frequently generated in genome-wide association studies (GWAS) and other studies. It is important to identify features such as single nucleotide polymorphisms (SNPs) in GWAS that are associated with a disease. Random forests represent a very useful approach for this purpose, using a variable importance score. This importance score has several shortcomings. We propose an alternative importance measure to overcome those shortcomings.

Results

We characterized the effect of multiple SNPs under various models using our proposed importance measure in random forests, which uses maximal conditional chi-square (MCC) as a measure of association between a SNP and the trait conditional on other SNPs. Based on this importance measure, we employed a pe

SUBMITTER: Wang M 

PROVIDER: S-EPMC2832825 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature