SDCL-AD: a structure-aware dual-graph contrastive learning framework for Alzheimer's disease prediction using multi-omics data

Introduction Early prediction of Alzheimer's disease (AD) is of great significance for disease intervention, personalized diagnosis, and treatment. With the rapid accumulation of multi-omics data, integrating complementary information from different molecular layers has become a key challenge in improving AD prediction. However, conventional methods have limitations in fully capturing modality-specific heterogeneity and latent relationships among samples. Methods To address these limitations, we propose SDCL-AD, a structure-aware dual-graph contrastive learning framework for AD prediction. SDCL-AD first constructs dual sample similarity graphs using proteomic and transcriptomic features, respectively, and then employs dual-graph autoencoders to learn low-dimensional representations that preserve modality-specific topological structures. On this basis, a cross-modal contrastive learning mechanism is introduced to align the representations of the same sample under different omics views, thereby enhancing the consistency and discriminability of shared disease representations. Finally, the learned structure-aware topological features are fused with the original multi-omics features to complete AD prediction. Results SDCL-AD achieved an AUC of 0.9157 and an AUPR of 0.9381, demonstrating superior predictive performance compared to baseline methods. Additional ablation studies and interpretability analyses further validated SDCL-AD's capability in integrating multi-omics data. Discussion These results suggest that SDCL-AD effectively integrates multi-omics information while capturing both modality-specific structures and cross-modal consistencies, providing a promising tool for AD prediction.