The wisdom of the commons: Ensemble tree classifiers for prostate cancer prognosis

James A. Koziol, Anne C. Feng, Zhenyu Jia, Yipeng Wang, Seven Goodison, Michael Mcclelland, Dan Mercola

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Motivation: Classification and regression trees have long been used for cancer diagnosis and prognosis. Nevertheless, instability and variable selection bias, as well as overfitting, are well-known problems of tree-based methods. In this article, we investigate whether ensemble tree classifiers can ameliorate these difficulties, using data from two recent studies of radical prostatectomy in prostate cancer. Results: Using time to progression following prostatectomy as the relevant clinical endpoint, we found that ensemble tree classifiers robustly and reproducibly identified three subgroups of patients in the two clinical datasets: non-progressors, early progressors and late progressors. Moreover, the consensus classifications were independent predictors of time to progression compared to known clinical prognostic factors.

Original languageEnglish (US)
Pages (from-to)54-60
Number of pages7
JournalBioinformatics
Volume25
Issue number1
DOIs
StatePublished - Jan 1 2009

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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