Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data | Zendy

Jiang Gui | Zendy; Hongzhe Li | Zendy

Open Access

Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data

Author(s) -

Jiang Gui,

Hongzhe Li

Publication year - 2005

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bti422

Subject(s) - categorical variable , proportional hazards model , microarray analysis techniques , regression , regression analysis , computational biology , microarray , survival analysis , gene chip analysis , sample size determination , expression (computer science) , phenotype , gene , gene expression , sample (material) , biology , data mining , computer science , statistics , genetics , mathematics , machine learning , chemistry , chromatography , programming language

An important application of microarray technology is to relate gene expression profiles to various clinical phenotypes of patients. Success has been demonstrated in molecular classification of cancer in which the gene expression data serve as predictors and different types of cancer serve as a categorical outcome variable. However, there has been less research in linking gene expression profiles to the censored survival data such as patients' overall survival time or time to cancer relapse. It would be desirable to have models with good prediction accuracy and parsimony property.

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