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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Graph neural networks on assurance case graphs reach 0.76 ROC-AUC for link prediction and 0.94 F1 for distinguishing human from LLM-generated cases, with observed differences in hierarchical linking patterns.
MolecBioNet, a graph neural network that treats drug pairs as unified entities with knowledge graph and molecular substructure views, reports state-of-the-art accuracy, F1, and PR-AUC on the Ryu and DrugBank DDI benchmarks.
A position paper arguing that the fragility of biomedical knowledge graphs stems from missing software engineering infrastructure, and cataloguing eight challenges, from data package managers to pipeline testing, that need to be solved.
citing papers explorer
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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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Evaluating Assurance Cases as Text-Attributed Graphs for Structure and Provenance Analysis
Graph neural networks on assurance case graphs reach 0.76 ROC-AUC for link prediction and 0.94 F1 for distinguishing human from LLM-generated cases, with observed differences in hierarchical linking patterns.
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Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations
MolecBioNet, a graph neural network that treats drug pairs as unified entities with knowledge graph and molecular substructure views, reports state-of-the-art accuracy, F1, and PR-AUC on the Ryu and DrugBank DDI benchmarks.
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Biomedical Knowledge Composition: A Software Engineering Perspective
A position paper arguing that the fragility of biomedical knowledge graphs stems from missing software engineering infrastructure, and cataloguing eight challenges, from data package managers to pipeline testing, that need to be solved.