GMC-DMA: GNN-Mamba Co-Contrastive Optimization for Disease-Metabolite Association Prediction

As a product of cellular metabolic activity, the level change of metabolites is closely related to the occurrence and development of diseases, so the prediction of metabolite-disease association is a key issue in biomedical research. Traditional methods face the challenges of insufficient long-range dependency modeling and poor interpretability. To address these challenges, we propose a dual-path dynamic contrastive learning framework integrating graph neural networks (GNN) and Mamba architectures, enhanced by fast Kolmogorov-Arnold networks (FastKAN) for metabolite-disease association prediction (GMC-DMA). First, we construct a multi-source heterogeneous network that contains similarity and known associations. Then, the residual graph convolutional Network (ResGCN) is designed to capture the local topological features, and the Mamba architecture is introduced to establish the selective state space model (SSM), which deals with the global dependency with linear time complexity and eliminates the over-smoothing problem of message passing. Then, the InfoNCE loss function is used to implement cross-modal contrast learning, and the sample imbalance problem is solved by the dynamic negative sampling strategy. Finally, the bilinear decoder enhanced by FastKAN outputs the correlation probability. A large number of experimental results show that the comprehensive performance of GMC-DMA is significantly better than that of the baseline methods, proving its effectiveness in predicting disease-related metabolites. In addition, the case studies have also confirmed that GMC-DMA has good reliability in discovering potential metabolites.