用跨组织甲基化数据，提升阿尔茨海默病早期检测的准确性与可重复性。
MethConvTransformer: A Deep Learning Framework for Cross-Tissue Alzheimer's Disease Detection
- 融合脑与外周组织甲基化数据，结合卷积与自注意力机制捕捉位点关联。
- 在6个GEO数据集和ADNI验证中均优于传统模型，区分度高且泛化性强。
- 揭示免疫信号、脂质代谢等与阿尔茨海默病相关的甲基化模式，适合生物机制研究。
阿尔茨海默病（AD）是一种多因素神经退行性疾病，以认知能力进行性下降和大脑广泛表观遗传紊乱为特征。DNA甲基化作为一种稳定而动态的表观遗传修饰，有望成为早期非侵入性生物标志物。然而，甲基化特征在不同组织和研究间差异显著，限制了其可重复性与转化应用。为此，我们提出MethConvTransformer，一种基于Transformer的深度学习框架，整合脑组织与外周组织的甲基化谱，实现生物标志物发现。该模型结合CpG位点级线性投影与卷积、自注意力层，捕捉位点间的局部与长程依赖关系，并引入个体水平协变量与组织嵌入，以分离共享与区域特异性甲基化效应。在六个GEO数据集和一个独立的ADNI验证队列中，模型持续优于传统机器学习基线，展现出优异的判别力与泛化能力。此外，通过线性投影、SHAP和Grad-CAM++的可解释性分析，揭示了与AD相关通路一致的生物学有意义甲基化模式，包括免疫受体信号、糖基化、脂质代谢以及内膜系统（内质网/高尔基体）组织。这些结果表明，MethConvTransformer可提供稳健的跨组织表观遗传生物标志物，并具备多层次可解释性，推动可重复的甲基化诊断发展，同时为疾病机制提供可检验假说。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by progressive cognitive decline and widespread epigenetic dysregulation in the brain. DNA methylation, as a stable yet dynamic epigenetic modification, holds promise as a noninvasive biomarker for early AD detection. However, methylation signatures vary substantially across tissues and studies, limiting reproducibility and translational utility. To address these challenges, we develop MethConvTransformer, a transformer-based deep learning framework that integrates DNA methylation profiles from both brain and peripheral tissues to enable biomarker discovery. The model couples a CpG-wise linear projection with convolutional and self-attention layers to capture local and long-range dependencies among CpG sites, while incorporating subject-level covariates and tissue embeddings to disentangle shared and region-specific methylation effects. In experiments across six GEO datasets and an independent ADNI validation cohort, our model consistently outperforms conventional machine-learning baselines, achieving superior discrimination and generalization. Moreover, interpretability analyses using linear projection, SHAP, and Grad-CAM++ reveal biologically meaningful methylation patterns aligned with AD-associated pathways, including immune receptor signaling, glycosylation, lipid metabolism, and endomembrane (ER/Golgi) organization. Together, these results indicate that MethConvTransformer delivers robust, cross-tissue epigenetic biomarkers for AD while providing multi-resolution interpretability, thereby advancing reproducible methylation-based diagnostics and offering testable hypotheses on disease mechanisms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。