摘要
针对生存分析中多组学数据带来的维数灾难和过拟合问题,提出了一种基于多组学数据和稀疏变分自编码器的生存分析算法VAESCox。该算法将变分自编码器的基本结构与稀疏编码和生存分析相结合,在无监督阶段训练变分自编码器学习低维表示,在监督阶段将训练的权重迁移到生存分析模型,并对传递权重进行微调和稀疏编码。实验结果表明,在八种不同癌症类型的数据集上,VAESCox模型在消融和对比实验中均取得了较高的C指数值。与其他四种基准生存分析方法相比,所提算法不仅缓解了多组学数据融合的过拟合问题,也显著提高了生存预测性能,表明不同组学数据的融合有助于预后生存结果的精准预测。
Aiming at the curse of dimensionality and overfitting issues caused by multi-omics data for survival analysis, this paper proposed a survival analysis model based on multi-omics data and sparse variational autoencoder, called VAESCox. The algorithm combined the basic structure of variational autoencoder with sparse coding and survival analysis, trained the variational autoencoder to learn low-dimensional representations in the unsupervised stage, transfered the trained weights to the survival analysis model, fine-tunes and sparse encodes the passed weights in the supervised stage. Experimental results show that VAESCox model achieved higher C-index values in the ablation and comparison experiments on 8 different cancer types datasets. Compared with other four baseline survival analysis methods, the proposed algorithm not only mitigates the overfitting issue of multi-omics data integration, but also significantly improves survival prediction performance, indicating that the integration of different omics data is beneficial for accurate prediction of prognostic survival outcome.
作者
殷清燕
武锐萍
陈旺旺
边根庆
Yin Qingyan;Wu Ruiping;Chen Wangwang;Bian Genqing(School of Science,Xi’an University of Architecture&Technology,Xi’an 710055,China;School of Information&Control Engineering,Xi’an University of Architecture&Technology,Xi’an 710055,China)
出处
《计算机应用研究》
CSCD
北大核心
2023年第3期771-775,共5页
Application Research of Computers
基金
国家自然科学基金资助项目(61872284,12001418)
陕西省自然科学基础研究计划面上项目(2022JM-026)。
关键词
生存分析
多组学数据融合
变分自编码器
稀疏编码
survival analysis
multi-omics data integration
variational autoencoders
sparse coding
