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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chi-Cheng Haung Shin-Hsin Tu Heng-Hui Lien Ching-Shui Huang Chuang, E.Y. Liang-Chuan Lai |
| Copyright Year | 2010 |
| Description | Author affiliation: Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taiwan (Chi-Cheng Haung; Heng-Hui Lien) || Graduate Institute of Physiology, National Taiwan University, Taiwan (Liang-Chuan Lai) || School of Medicine, Fu-Jen Catholic University and Cathay General hospital, Taiwan (Shin-Hsin Tu) || Department of Surgery, Cathay General Hospital, Taiwan (Chuang, E.Y.) || School of Medicine, Taipei Medical University, Taiwan (Ching-Shui Huang) |
| Abstract | The study was to compare principle component (PC) versus partial least square (PLS) regression, the former unsupervised and the latter supervised gene component analysis, for highly complicated and correlated microarray gene expression profile. Projection of derived classifiers into independent samples for clinical phenotype prediction was evaluated as well. Previous studies had suggested that PLS might be superior to PC regression in the task of tumor classification since the covariance between predictive and respondent variables was maximized for latent factor extraction. We applied both algorithms for classifier construction and validated their prediction performance on independent microarray experiments. The statistical strategy could reduce high-dimensionality of microarray features and avoid the collinearity problem inherited in gene expression profiles. Proposed predictive model could discriminate breast cancers with positive and negative estrogen receptor status successfully and was feasible for both Taiwanese and Chinese females, both with the same Han Chinese ethnic origin. |
| Starting Page | 506 |
| Ending Page | 511 |
| File Size | 618399 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424483037 |
| e-ISBN | 9781424483044 |
| DOI | 10.1109/BIBMW.2010.5703853 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Accuracy Breast cancer Classification algorithms Electronic mail Gene expression Erbium |
| Content Type | Text |
| Resource Type | Article |
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