Penalized logistic regression for high-dimensional DNA methylation data with case-control studies

被引：75

作者：

Sun, Hokeun ^{[1
]}

Wang, Shuang ^{[1
]}

机构：

[1] Columbia Univ, Mailman Sch Publ Hlth, Dept Biostat, New York, NY 10032 USA

来源：

BIOINFORMATICS | 2012年 / 28卷 / 10期

关键词：

VARIABLE SELECTION; REGULARIZATION; LASSO;

D O I：

10.1093/bioinformatics/bts145

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Results: Using simulation studies we demonstrated that the proposed procedure outperforms existing main-stream regularization methods such as lasso and elastic-net when data is correlated within a group. We also applied our method to identify important CpG sites and corresponding genes for ovarian cancer from over 20 000 CpGs generated from Illumina Infinium HumanMethylation27K Beadchip. Some genes identified are potentially associated with cancers.

引用

页码：1368 / 1375

页数：8

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