{
  "id": 2216,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1851917",
  "title": "RAG-GNN: retrieval-augmented graph neural networks for protein interaction network embeddings",
  "summary": "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",
  "authors": "Hasi Hays",
  "category": "research",
  "topics": "safety-alignment,finance-investment,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-10T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/2216",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1851917",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}