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DeepStrataAge:一个可解释的深度学习时钟,它揭示了不同阶段和性别的DNA甲基化衰老动态

Aaron Lin^1,2, Ilinca Giosan¹, Andrea Aparicio³

¹TruDiagnostic, 881 Corporate Drive, Lexington, KY, USA.

|March 14, 2026

概括

我们开发了一个深度神经网络 (DNN) 的DNA甲基化钟,以准确测量生物年龄. 这种新的时钟揭示了特定于性别的"衰老波",并提供了对衰老过程的机械洞察力.

科学领域:

表观遗传学和计算生物学
基因组学和衰老研究研究.
系统生物学和多学科的系统生物学.

背景情况:

老龄化是主要慢性疾病的主要危险因素.
当前的DNA甲基化 (DNAm) 时钟通常使用线性模型,缺少复杂的生物动态.
需要更准确和机械解释的生物年龄测量方法.

研究的目的:

开发基于深度神经网络 (DNN) 的DNAm时钟,用于精确的生物年龄估计.
确定表观遗传衰老中的非线性动态和性别特异性模式.
通过可解释的AI,揭示对衰老过程的机械洞察力.

主要方法:

在使用Illumina EPIC v1.0和v2.0数组的29,167个样本上训练了一个DNN模型.
根据性别和年龄分层的相关性,选择了12234个CPG.
为了模型的可解释性和揭示表观遗传动力学,采用了沙普利增量解释 (SHAP).

主要成果:

在生物年龄预测 (1.89年) 中实现了高精度,超过了现有的钟表.
在影响年龄的CpG中发现了阶段结构,波形动态,具有明显的性别特定时间.
确定了与发育,细胞骨,免疫和转录途径相关的性别特异性表观遗传衰老阶段.

结论:

基于DNN的DNAm时钟为生物年龄提供了卓越的准确性和机械解释性.
表观遗传衰老表现出非线性,性别特异的波形模式,与多原子衰老特征保持一致.
这一框架揭示了快速分子衰老的关键性别特定窗口,推动了我们对与年龄有关的疾病的理解.

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