Estrogen receptor status prediction by gene component regression: A comparative study

Chi Cheng Huang, Eric Y. Chuang, Shih Hsin Tu, Heng Hui Lien, Jaan Yeh Jeng, Jung Sen Liu, Ching Shui Huang, Liang Chuan Lai

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The aim of the study is to evaluate gene component analysis for microarray studies. Three dimensional reduction strategies, Principle Component Regression (PCR), Partial Least Square (PLS) and Reduced Rank Regression (RRR) were applied to publicly available breast cancer microarray dataset and the derived gene components were used for tumor classification by Logistic Regression (LR) and Linear Discriminative Analysis (LDA). The impact of gene selection/filtration was evaluated as well. We demonstrated that gene component classifiers could reduce the high-dimensionality of gene expression data and the collinearity problem inherited in most modern microarray experiments. In our study gene component analysis could discriminate Estrogen Receptor (ER) positive breast cancers from negative cancers and the proposed classifiers were successfully reproduced and projected into independent microarray dataset with high predictive accuracy.

Original languageEnglish
Pages (from-to)149-171
Number of pages23
JournalInternational Journal of Data Mining and Bioinformatics
Issue number2
Publication statusPublished - 2014
Externally publishedYes


  • Breast cancer
  • Dimension reduction
  • Estrogen receptor
  • Gene component
  • Microarray
  • Partial least square
  • Principle component regression

ASJC Scopus subject areas

  • Information Systems
  • General Biochemistry,Genetics and Molecular Biology
  • Library and Information Sciences


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