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VAE-Surv:一种基于基因的聚类和预后预测在骨髓质疏松症候群的新方法

Cesare Rollo¹, Corrado Pancotti¹, Flavio Sartori¹

¹Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, Via Santena 19, 10126, Torino, Italy.

Computer methods and programs in biomedicine

|January 28, 2025

概括

VAE-Surv是一种新的计算框架,通过整合分子和临床数据,有效地分层患者并预测预后. 这种方法在生存预测中优于传统方法,并识别出新的患者亚型,以更好地管理疾病.

科学领域:

生物医学数据分析
计算生物学是一种计算生物学.
机器学习在医疗保健中的应用

背景情况:

当前的患者分层和预后预测方法经常独立分析数据,限制了全面的理解.
由于数据异质性,多模式性和高维度性,聚类生存数据具有挑战性,特别是在生物医学应用中.

研究的目的:

引入VAE-Surv,这是一个用于患者分层和预后预测的多式联络计算框架.
为了解决复杂的生物医学数据的现有生存数据聚类方法的局限性.

主要方法:

VAE-Surv集成了一个变异自编码器 (VAE) 用于分子数据的维度缩小与使用临床特征的深度生存模型.
VAE嵌入优先考虑局部连贯性以检测非线性关系,并用于K-means集群的隐藏表示.
该框架应用于骨髓质疏松症候群 (MDS) 队列,将结果与世卫组织分类和考克斯模型进行比较.

主要成果:

在GenomMed4All队列中,VAE-Surv的C指数中位数为0.78,超过了传统方法.
与直接输入数据集群相比,潜伏空间提高了集群性能.
确定的集群捕获了现有的临床分类和建议的新型数据驱动的患者组,在外部MDS队列上得到验证 (中位数C指数=0.74).

结论:

VAE-Surv自动化了患者集群识别,在生存预测方面超过了CoxPH模型.
在MDS中基于基因的集群表明了明显的生存分层.
临床信息的整合使得预后预测的高性能成为可能,同时保持了可解释性.

关键词:

深度学习 (Deep Learning) 是一种深度学习.基于基因的聚类.骨髓质疏松性综合征是什么?骨髓质疏松性综合征对生存分析的分析.变量自动编码器变量自动编码器

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