RAG-GNN: Integrating Retrieved Knowledge with Graph Neural Networks for Precision Medicine

Network topology excels at structural predictions but fails to capture functional semantics encoded in biomedical literature. We present RAG-GNN, an end-to-end trainable retrieval-augmented graph neural network framework that integrates GNN representations with dynamically retrieved literature-derived knowledge through a jointly optimized retrieval projection, gated fusion mechanism, and contrastive alignment. In a cancer signaling case study (379 proteins, 3,498 interactions, 14 functional categories), RAG-GNN improves functional clustering from silhouette = -0.237 ± 0.065 (GNN-only) to -0.144 ± 0.066, a consistent improvement of +0.093 ± 0.022 across 10 random seeds, while the learned retrieval achieves mean precision@10 = 0.242, a 152% improvement over the random baseline (0.096). Heuristic information decomposition with bootstrap confidence intervals reveals that topology and retrieval encode overwhelmingly shared information (95.6%), with retrieval improving both intra-cluster cohesion (silhouette) and cluster agreement (ARI +0.021 ± 0.015). Counterfactual experiments confirm that adversarial, absent, and random retrieval all degrade performance, validating that the gated fusion mechanism depends on document content. Benchmarking against eight established embedding methods demonstrates task-specific complementarity: topology-focused methods achieve strong link prediction, while retrieval augmentation consistently improves functional clustering within the controlled GNN-only ablation. DDR1 subnetwork analysis provides confirmatory validation consistent with established synthetic lethality relationships. These results establish that topology-only and retrieval-augmented approaches serve complementary purposes for precision medicine applications.