RAG-GNN: retrieval-augmented graph neural networks for protein interaction network embeddings
We present RAG-GNN, an end-to-end trainable framework that augments a graph neural network (GNN) encoder for protein interaction networks with a jointly optimized dense retrieval module over a TF-IDF-indexed document corpus, a gated fusion mechanism, and contrastive alignment between node and document representations. The study is positioned as a controlled methodological investigation of whether retrieval augmentation provides measurable benefit beyond a matched GNN-only ablation, rather than a
Record details
Published: 10 July 2026
Source: Frontiers in Artificial Intelligence
Category: Research
Topics: Safety & alignment · Finance, VC & PE · Biotech
Retrieved: 14 July 2026
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ethics.ai (10 July 2026), “RAG-GNN: retrieval-augmented graph neural networks for protein interaction network embeddings,” evidence record 2216, https://ethics.ai/record/2216 (originally published by Frontiers in Artificial Intelligence).
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