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

研究成果: 雜誌貢獻文章同行評審

2 引文 斯高帕斯(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.

頁(從 - 到)149-171
期刊International Journal of Data Mining and Bioinformatics
出版狀態已發佈 - 2014

ASJC Scopus subject areas

  • 圖書館與資訊科學
  • 資訊系統
  • 生物化學、遺傳與分子生物學 (全部)


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