Multiclass prediction with partial least square regression for gene expression data: Applications in breast cancer intrinsic taxonomy

Chi Cheng Huang, Shih Hsin Tu, Ching Shui Huang, Heng Hui Lien, Liang Chuan Lai, Eric Y. Chuang

研究成果: 雜誌貢獻文章同行評審

7 引文 斯高帕斯(Scopus)

摘要

Multiclass prediction remains an obstacle for high-throughput data analysis such as microarray gene expression profiles. Despite recent advancements in machine learning and bioinformatics, most classification tools were limited to the applications of binary responses. Our aim was to apply partial least square (PLS) regression for breast cancer intrinsic taxonomy, of which five distinct molecular subtypes were identified. The PAM50 signature genes were used as predictive variables in PLS analysis, and the latent gene component scores were used in binary logistic regression for each molecular subtype. The 139 prototypical arrays for PAM50 development were used as training dataset, and three independent microarray studies with Han Chinese origin were used for independent validation (n = 535). The agreement between PAM50 centroid-based single sample prediction (SSP) and PLS-regression was excellent (weighted Kappa: 0.988) within the training samples, but deteriorated substantially in independent samples, which could attribute to much more unclassified samples by PLS-regression. If these unclassified samples were removed, the agreement between PAM50 SSP and PLS-regression improved enormously (weighted Kappa: 0.829 as opposed to 0.541 when unclassified samples were analyzed). Our study ascertained the feasibility of PLS-regression in multi-class prediction, and distinct clinical presentations and prognostic discrepancies were observed across breast cancer molecular subtypes.

原文英語
文章編號248648
期刊BioMed Research International
2013
DOIs
出版狀態已發佈 - 2013

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

指紋 深入研究「Multiclass prediction with partial least square regression for gene expression data: Applications in breast cancer intrinsic taxonomy」主題。共同形成了獨特的指紋。

引用此