Gene selection using iterative feature elimination random forests for survival outcomes

Herbert Pang*, Stephen L. George, Ken Hui, Tiejun TONG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)

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 random forests to identify a set of prognostic genes. We compare our method with several machine learning methods and various node split criteria using several real data sets. Our method performed well in both simulations and real data analysis. Additionally, we have shown the advantages of our approach over single-gene-based approaches. Our method incorporates multivariate correlations in microarray data for survival outcomes. The described method allows us to better utilize the information available from microarray data with survival outcomes.

Original languageEnglish
Article number6193092
Pages (from-to)1422-1431
Number of pages10
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume9
Issue number5
DOIs
Publication statusPublished - 2012

Scopus Subject Areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

User-Defined Keywords

  • Cancer
  • gene selection
  • iterative feature elimination
  • microarrays
  • random forest
  • survival

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