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

ArXi:2602.00586v2 Announce Type: replace-cross 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.