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

• URL: http://arxiv.org/abs/2601.00143v1
• Date: Thu, 01 Jan 2026 00:18:33 GMT
• Title: MethConvTransformer: A Deep Learning Framework for Cross-Tissue Alzheimer's Disease Detection
• Authors: Gang Qu, Guanghao Li, Zhongming Zhao,
• Abstract summary: Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by progressive cognitive decline and widespread dysregulation in the brain.<n>MethConvTransformer is a transformer-based deep learning framework that integrates DNA methylation profiles from both brain and peripheral tissues.
• Score: 4.931890971425293
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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