Gene selection using iterative feature elimination random forests for survival outcomes.

ABSTRACT: Although many feature selection methods for classification have been developed, there is a need to identify genes in high-dimensional data with censored survival outcomes. Traditional methods for gene selection in classification problems have several drawbacks. First, the majority of the gene selection approaches for classification are single-gene based. Second, many of the gene selection procedures are not embedded within the algorithm itself. The technique of random forests has been found to perform well in high-dimensional data settings with survival outcomes. It also has an embedded feature to identify variables of importance. Therefore, it is an ideal candidate for gene selection in high-dimensional data with survival outcomes. In this paper, we develop a novel method based on the ran

SUBMITTER: Pang H 

PROVIDER: S-EPMC3495190 | biostudies-literature | 2012 Sep-Oct

REPOSITORIES: biostudies-literature