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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-deriv
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