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arXiv cs.LG
arXiv cs.LG
6/26/2026
KG-TRACE: A Neuro-Symbolic Framework for Mechanistic Grounding in Antimicrobial Resistance Prediction

KG-TRACE: A Neuro-Symbolic Framework for Mechanistic Grounding in Antimicrobial Resistance Prediction

Short summary

KG-TRACE combines machine learning with biological knowledge graphs to predict drug-resistant tuberculosis with both high accuracy (AUROC 0.9760) and clinical interpretability. The framework weights neural predictions against established pathways through a learned trust gate, achieving 92.5% symbolic grounding. This bridges predictive accuracy with clinical trust through verifiable audit trails.

  • •Neuro-symbolic framework integrates genomic ML with WHO mutation knowledge graphs
  • •Achieves 0.9760 AUROC on TB resistance while maintaining 92.5% biological grounding
  • •Provides clinically verifiable attribution trails for medical decision support

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Read full article at arXiv cs.LG

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