Gene selection in cox regression model based on a new adaptive elastic net penalty
IRAQI JOURNAL OF STATISTICAL SCIENCES,
2020, Volume 17, Issue 32, Pages 27-36
AbstractRegression analysis is great of interest in several studies, especially in medicine. The Cox regression model is one of the most important models of regression used in the medical field. It is the tool by which the dependent variable is modeled when the values of that variable are in the form of survival time data. As in linear regression model, the Cox regression model may contain many explanatory variables, which negatively affects the accuracy of the model and its simplicity in interpreting the results. 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.
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