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.
Original language | English |
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Pages (from-to) | 149-171 |
Number of pages | 23 |
Journal | International Journal of Data Mining and Bioinformatics |
Volume | 9 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Keywords
- 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