Survival Analysis with High-Dimensional Omics Data Using a Threshold Gradient Descent Regularization-Based Neural Network Approach

被引:1
作者
Fan, Yu [1 ,2 ]
Zhang, Sanguo [1 ,2 ]
Ma, Shuangge [3 ]
机构
[1] Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
[2] Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China
[3] Yale Sch Publ Hlth, Dept Biostat, New Haven, CT 06511 USA
基金
中国国家自然科学基金;
关键词
survival analysis; high-dimensional omics data; neural network; TGDR; SELECTION; CLASSIFICATION; SIGNATURE;
D O I
10.3390/genes13091674
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Analysis of data with a censored survival response and high-dimensional omics measurements is now common. Most of the existing analyses are based on specific (semi)parametric models, in particular the Cox model. Such analyses may be limited by not having sufficient flexibility, for example, in accommodating nonlinearity. For categorical and continuous responses, neural networks (NNs) have provided a highly competitive alternative. Comparatively, NNs for censored survival data remain limited. Omics measurements are usually high-dimensional, and only a small subset is expected to be survival-associated. As such, regularized estimation and selection are needed. In the existing NN studies, this is usually achieved via penalization. In this article, we propose adopting the threshold gradient descent regularization (TGDR) technique, which has competitive performance (for example, when compared to penalization) and unique advantages in regression analysis, but has not been adopted with NNs. The TGDR-based NN has a highly sensible formulation and an architecture different from the unregularized and penalization-based ones. Simulations show its satisfactory performance. Its practical effectiveness is further established via the analysis of two cancer omics datasets. Overall, this study can provide a practical and useful new way in the NN paradigm for survival analysis with high-dimensional omics measurements.
引用
收藏
页数:46
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