Penalized logistic regression for high-dimensional DNA methylation data with case-control studies
Penalized logistic regression for high-dimensional DNA methylation data with case-control studies
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DOI：
10.1093/bioinformatics/bts145
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发表时间：
2012-05-15
期刊：
影响因子：
5.8
通讯作者：
Wang, Shuang
中科院分区：
文献类型：
--
作者：
Sun, Hokeun;Wang, Shuang
结果如下：使用模拟研究，我们证明了所提出的程序优于现有的主流正则化方法，如套索和弹性网络，当数据在一个组内相关。我们还应用我们的方法从Illumina Infinium HumanMethylation 27K Beadchip产生的超过20 000个CpG中鉴定了卵巢癌的重要CpG位点和相应基因。一些被发现的基因可能与癌症有关。
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.