Adaptive Graph Prompting Meets Contrastive Learning: A Multi-View Framework for Metabolite-Disease Association Prediction.

Metabolite-disease associations (MDAs) are critical for advancing precision medicine, yet existing computational methods face challenges in data sparsity, noise robustness, and feature representation. We propose GPLCL (graph prompt-enhanced contrastive learning), a novel multi-view graph learning framework integrating adaptive graph prompting and contrastive learning. GPLCL introduces enhanced graph prompt features (GPF +) with attention-based node adaptation, enabling dynamic feature recalibration. Through strategic graph augmentation and self-supervised contrastive optimization, it preserves essential topological invariants while aggregating multi-scale neighborhood patterns via HeteroGraphSAGE. In the fivefold cross-validation, GPLCL achieves AUC 0.9761 and AUPR 0.9729 on dataset 1, which is the highest improvement of 0.55 to 6.37 percentage points over the existing methods; GPLCL still maintains AUC 0.9576 and AUPR 0.9499 on the highly noisy Dataset 2, which proves its excellent performance and robustness. Case studies on type 1 diabetes, obesity, and Parkinson's disease highlighted the model's potential in discovering novel MDAs, underscoring its applicability in advancing metabolomics research and translational medicine. The code is publicly available at https://github.com/yxue9/GPLCL .