Variable selection using the Lasso-Cox model with Bayesian regularization

Selection of prognostic genes associated with tumor has been a subject of considerable research in recent years. In order to solve the high-dimensional gene expression profiles, the Lasso-Cox model has been proposed and widely used in survival analysis. Based on the sparse regression algorithm, the...

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Bibliographic Details
Published inIEEE Conference on Industrial Electronics and Applications (Online) pp. 924 - 927
Main Authors Lu, Wenxin, Yu, Zhuliang, Gu, Zhenghui, Huang, Jinhong, Gao, Wei, Zhou, Haiyu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2018
Subjects
Adaptation models
Analytical models
Bayes methods
Bayesian regularization
Hazards
Input variables
Lasso-Cox regression
Numerical models
Prognostic gene
Survival analysis
Tumors
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ISSN2158-2297
DOI10.1109/ICIEA.2018.8397844

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Summary:Selection of prognostic genes associated with tumor has been a subject of considerable research in recent years. In order to solve the high-dimensional gene expression profiles, the Lasso-Cox model has been proposed and widely used in survival analysis. Based on the sparse regression algorithm, the regularization parameter must be carefully tuned by cross-validation to optimize performance. In this paper, we introduce an algorithm based on simple Bayesian approach to replace the process of parameter selection, and the regularization parameter is determined adaptively in training. Simulation results show that variable selection of Bayesian-Lasso (BLasso) can be more accurate than that of Lasso method. We also apply our algorithm to a real dataset DLBCL, and the selected genes have been proven to have close relationship with the tumor.
ISSN:2158-2297
DOI:10.1109/ICIEA.2018.8397844