title: Estrogen receptor status prediction by gene component regression: a comparative study.
abstract: 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.
authors.forename: Chi-Cheng
authors.initials: CC
authors.lastname: Huang
authors.forename: Shih-Hsin
authors.initials: SH
authors.lastname: Tu
authors.forename: Heng-Hui
authors.initials: HH
authors.lastname: Lien
authors.forename: Jaan-Yeh
authors.initials: JY
authors.lastname: Jeng
authors.forename: Jung-Sen
authors.initials: JS
authors.lastname: Liu
authors.forename: Ching-Shui
authors.initials: CS
authors.lastname: Huang
authors.forename: Liang-Chuan
authors.initials: LC
authors.lastname: Lai
authors.forename: Eric Y
authors.initials: EY
authors.lastname: Chuang
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D030541:Databases, Genetic; D005260:Female; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D011960:Receptors, Estrogen / Q000235:genetics / Q000378:metabolism
chemical_list: D009363:Neoplasm Proteins; D011960:Receptors, Estrogen