MFC-GCN: Identifying Disease Driver Genes via Graph Neural Networks and Multi-Network Fusion Contrastive Learning

The identification of disease-driving genes is crucial for understanding disease mechanisms and advancing precision medicine. However, current methods for integrating heterogeneous biological data face challenges, including suboptimal feature representation and inadequate modeling of cross-modal dependencies, particularly in capturing latent multi-level inter-actions within complex biological networks. To address this, we propose MFC-GCN, a framework that integrates five biological networks—Protein-Protein Interaction, KEGG pathway co-occurrence, Gene Ontology semantic similarity, gene sequence similarity, and gene co-expression—using multidimensional enhancement, cross-network contrastive learning, and adaptive expert selection. A hierarchical graph convolutional encoder extracts deep representations from each modality, which are fused via an attention-guided mixture-of-experts gating network. A bi-directional contrastive learning mechanism further enhances the model's discriminative power. In five-fold cross-validation, our model achieved an average AUROC of 91% and AUPRC of 84%. Ablation studies confirm its ability to capture gene network dependencies and interactions, aiding in the understanding of disease driver genes. MFC-GCN is available at https://github.com/HaoTongXueWang/MFC-GCN.