MuloAD: A Multiomics Integration Model Utilizing Graph Convolutional Networks for Alzheimer's Disease Diagnosis and Biomarker Identification

Abstract

Early and accurate diagnosis of Alzheimer's disease (AD) remains challenging due to the limitations of single biomarker approaches and the complexity of disease pathogenesis. In this study, we present MuloAD, a graph convolutional neural network-based framework that integrates multiomics data for enhanced AD diagnosis and biomarker identification. The MuloAD leverages GraphSAGE to extract features from DNA methylation, mRNA expression and microRNA expression data independently, followed by a view correlation discovery network that captures cross-omics relationships in a higher dimensional label space. Using 350 samples from the ROSMAP cohort, MuloAD demonstrates superior classification performance compared to existing multiomics methods, delivering robust accuracy across different omics combinations. Feature-importance analysis identifies several key molecular biomarkers that exhibit significant differential expression between patients with AD and healthy controls. The results of ablation studies confirm the complementary value of multiomics integration compared to single-omics approaches. These findings highlight the potential of deep learning-driven multiomics integration in the diagnosis of neurodegenerative diseases and offer novel molecular targets for AD detection and therapeutic development.