MOGAD: Integrated Multi-Omics and Graph Attention for the Discovery of Alzheimer’s Disease’s Biomarkers

作者：Zhizhong Zhang, Yuqi Chen, Changliang Wang, Maoni Guo, Cai Lu, Jian He, Yanchun Liang, Garry Wong, Liang Chen · 发表于：Informatics · 年份：2025 · DOI：10.3390/informatics12030068 · 被引用次数：3 · 研究领域：Bioinformatics and Genomic Networks、Computational Drug Discovery Methods、Gene expression and cancer classification

The selection of appropriate biomarkers in clinical practice aids in the early detection, treatment, and prevention of disease while also assisting in the development of targeted therapeutics. Recently, multi-omics data generated from advanced technology platforms has become available for disease studies. Therefore, the integration of this data with associated clinical data provides a unique opportunity to gain a deeper understanding of disease. However, the effective integration of large-scale multi-omics data remains a major challenge. To address this, we propose a novel deep learning model—the Multi-Omics Graph Attention biomarker Discovery network (MOGAD). MOGAD aims to efficiently classify diseases and discover biomarkers by integrating various omics data such as DNA methylation, gene expression, and miRNA expression. The model consists of three main modules: Multi-head GAT network (MGAT), Multi-Graph Attention Fusion (MGAF), and Attention Fusion (AF), which work together to dynamically model the complex relationships among different omics layers. We incorporate clinical data (e.g., APOE genotype) which enables a systematic investigation of the influence of non-omics factors on disease classification. The experimental results demonstrate that MOGAD achieves a superior performance compared to existing single-omics and multi-omics integration methods in classification tasks for Alzheimer’s disease (AD). In the comparative experiment on the ROSMAP dataset, our model achieve...