RAG-GNN：将检索到的知识与图神经网络结合用于精准医学
RAG-GNN: Integrating Retrieved Knowledge with Graph Neural Networks for Precision Medicine
AI总结：
RAG-GNN通过整合图神经网络与动态检索的文献知识，提升了癌症信号功能聚类的精度，同时验证了检索增强对精准医学应用的互补性。
摘要：
网络拓扑在结构预测上表现优异，但无法捕捉生物医学文献中的功能语义。我们提出了RAG-GNN，一种端到端可训练的检索增强图神经网络框架，通过联合优化的检索投影、门控融合机制和对比对齐，将GNN表示与动态检索的文献知识相结合。在癌症信号案例研究（379个蛋白质，3,498个相互作用，14个功能类别）中，RAG-GNN将功能聚类从silhouette=-0.237±0.065（仅GNN）提升到-0.144±0.066，10次随机种子下一致改进+0.093±0.022。所学检索达到mean precision@10=0.242，比随机基线（0.096）提升152%。启发式信息分解与bootstrap置信区间显示，拓扑和检索编码了大量共享信息（95.6%），检索提升了聚类内凝聚力（silhouette）和聚类一致性（ARI+0.021±0.015）。反事实实验验证了对抗性、缺失性和随机性检索会降低性能，证明门控融合机制依赖于文档内容。与八种已建立的嵌入方法的基准测试显示，任务特定的互补性：拓扑聚焦方法在链接预测上表现强劲，而检索增强一致改善功能聚类在受控GNN-only消融中的表现。DDR1亚网络分析提供了与已建立的合成致死关系一致的验证。这些结果证明了拓扑仅和检索增强方法在精准医学应用中的互补性。
英文摘要：
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 $= -0.237 \pm 0.065$ (GNN-only) to $-0.144 \pm 0.066$, a consistent improvement of $+0.093 \pm 0.022$ 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 $+0.021 \pm 0.015$). 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.
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