MethConvTransformer: A Deep Learning Framework for Cross-Tissue Alzheimer's Disease Detection

TL;DR

MethConvTransformer is a novel deep learning framework that integrates cross-tissue DNA methylation data to improve early Alzheimer's disease detection and interpretability of epigenetic biomarkers.

Contribution

It introduces a transformer-based model combining local and long-range CpG dependencies with tissue and subject covariates for enhanced biomarker discovery.

Findings

Outperforms traditional machine learning models in discrimination accuracy

Achieves robust generalization across multiple datasets

Identifies biologically meaningful methylation patterns linked to AD pathways

Abstract

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.