Gene selection in Cox regression model based on a new adaptive elastic net penalty

Abstract EN

The common issues of high dimensional gene expression data for survival analysis are that many of genes may not be relevant to their diseases.

gene selection has been proved to be an effective way to improve the result of many methods.

the cox regression model is the most popular model in regression analysis for censored survival data.

in this paper, a new adaptive elastic net penalty with cox regression model is proposed, with the aim of identification relevant genes and provides high classification accuracy, by combining the cox regression model with the weighted l1-norm.

experimental results show that the proposed method significantly outperforms two competitor methods in terms of the area under the curve and the number of the selected genes.