Dual-channel learning framework for microbe-disease association prediction via subgraph enhanced dynamic weight–calibrated graph convolutional and cross-pooling attention networks

Abstract

Identifying potential microbe–disease associations (MDAs) is vital for elucidating disease pathogenesis and advancing precision medicine. Existing methods primarily learn features from heterogeneous microbe–disease graphs, but often rely solely on global topology for feature propagation, ignoring neighborhood subgraph density, centrality, and edge-weight heterogeneity. The loss of such structural information further exacerbates distributional shifts of microbe–disease feature representations, making it difficult for static concatenation or average fusion to effectively bridge the semantic gaps between modalities or accurately capture the contextual dependencies between node pairs. To address these challenges, we propose STDCAMDA, a dual-channel learning framework for MDA prediction. For structural enhancement, we design a subgraph topology module that fuses global and local topological information via multidimensional edge weights and node-gating mechanisms, thereby encoding rare structural signals while suppressing noise. In feature learning, we adopt a dual-channel strategy: embedding dynamic weight correction into a graph convolutional network for adaptive adjacency calibration and building a cross-pooling attention network to mitigate modality distribution shifts and capture cross-modal dependencies. Finally, we introduce two strategies: a dynamically weighted fusion classifier that integrates dual-channel features and uses a multi-layer perceptron for prediction, and a subgraph-aware negative sampling strategy that selects hard negative samples. Experiments on the Disbiome and HMDAD datasets demonstrate that STDCAMDA outperforms seven existing MDA prediction models, with statistical significance tests confirming the reliability of these improvements and cold-start evaluations validating its robustness and generalization capability. Practically, STDCAMDA provides an effective computational framework for prioritizing candidate disease-related microbes and supporting downstream biomedical validation.