Pathway hunting by random survival forests

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

Motivation: Pathway or gene set analysis has been widely applied to genomic data. Many current pathway testing methods use univariate test statistics calculated from individual genomic markers, which ignores the correlations and interactions between candidate markers. Random forests-based pathway analysis is a promising approach for incorporating complex correlation and interaction patterns, but one limitation of previous approaches is that pathways have been considered separately, thus pathway cross-talk information was not considered.Results: In this article, we develop a new pathway hunting algorithm for survival outcomes using random survival forests, which prioritize important pathways by accounting for gene correlation and genomic interactions. We show that the proposed method performs favourably compared with five popular pathway testing methods using both synthetic and real data. We find that the proposed methodology provides an efficient and powerful pathway modelling framework for high-dimensional genomic data.

Original languageEnglish
Pages (from-to)99-105
Number of pages7
JournalBioinformatics
Volume29
Issue number1
DOIs
StatePublished - Jan 1 2013

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Pathway
Genes
Testing
Genomics
Statistics
Interaction
Gene
Forests
Random Forest
Crosstalk
Test Statistic
Univariate
High-dimensional
Methodology
Modeling

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability
  • Medicine(all)

Cite this

Pathway hunting by random survival forests. / Chen, Xi; Ishwaran, Hemant.

In: Bioinformatics, Vol. 29, No. 1, 01.01.2013, p. 99-105.

Research output: Contribution to journalArticle

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