GRL-Safety benchmark shows that safety in graph representation learning depends on interactions between method design and specific graph stresses rather than broad method families.
A deep learning approach to antibiotic discovery.Cell, 180(4):688–702
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
KG-TRACE fuses genomic features with RotatE KG embeddings via an epistemic trust gate for AMR prediction, reporting 0.976 AUROC on isoniazid resistance in the CRyPTIC cohort plus 92.5% symbolic coverage via a new Biological Grounding Ratio metric.
RAG-GNN augments GNNs with retrieved literature knowledge via gated fusion to improve functional clustering of 379 proteins in cancer signaling networks, raising silhouette score by 0.093.
AIBuildAI-2 introduces a knowledge-enhanced agent with a hierarchical evolving external knowledge base that dynamically loads relevant AI development expertise, achieving first place on MLE-Bench at 70.7% medal rate.
citing papers explorer
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On the Safety of Graph Representation Learning
GRL-Safety benchmark shows that safety in graph representation learning depends on interactions between method design and specific graph stresses rather than broad method families.
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KG-TRACE: A Neuro-Symbolic Framework for Mechanistic Grounding in Antimicrobial Resistance Prediction
KG-TRACE fuses genomic features with RotatE KG embeddings via an epistemic trust gate for AMR prediction, reporting 0.976 AUROC on isoniazid resistance in the CRyPTIC cohort plus 92.5% symbolic coverage via a new Biological Grounding Ratio metric.
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RAG-GNN: Integrating Retrieved Knowledge with Graph Neural Networks for Precision Medicine
RAG-GNN augments GNNs with retrieved literature knowledge via gated fusion to improve functional clustering of 379 proteins in cancer signaling networks, raising silhouette score by 0.093.
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AIBuildAI-2: A Knowledge-Enhanced Agent for Automatically Building AI Models
AIBuildAI-2 introduces a knowledge-enhanced agent with a hierarchical evolving external knowledge base that dynamically loads relevant AI development expertise, achieving first place on MLE-Bench at 70.7% medal rate.