Merging microarray data, robust feature selection, and predicting prognosis in prostate cancer

Jing Wang, Anh Do Kim, Sijin Wen, Spyros Tsavachidis, Timothy J. McDonnell, Christopher J. Logothetis, Kevin R. Coombes

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Motivation: Individual microarray studies searching for prognostic biomarkers often have few samples and low statistical power; however, publicly accessible data sets make it possible to combine data across studies. Method: We present a novel approach for combining microarray data across institutions and platforms. We introduce a new algorithm, robust greedy feature selection (RGFS), to select predictive genes. Results: We combined two prostate cancer microarray data sets, confirmed the appropriateness of the approach with the Kolmogorov-Smirnov goodness-of-fit test, and built several predictive models. The best logistic regression model with stepwise forward selection used 7 genes and had a misclassification rate of 31%. Models that combined LDA with different feature selection algorithms had misclassification rates between 19% and 33%, and the sets of genes in the models varied substantially during cross-validation. When we combined RGFS with LDA, the best model used two genes and had a misclassification rate of 15%. Availability: Affymetrix U95Av2 array data are available at The cDNA microarray data are available through the Stanford Microarray Database ( GeneLink software is freely available at DNA-Chip Analyzer software is publicly available at

Original languageEnglish (US)
Pages (from-to)87-97
Number of pages11
JournalCancer Informatics
StatePublished - 2006
Externally publishedYes


  • Combining data
  • Cross-validation
  • Feature selection
  • Microarray expression profiling
  • Predictive model
  • Prostrate cancer

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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