aceMulti-omics-driven predictive modelling of antimicrobial resistance emergence in African food value chains under climate change scenarios.
Antimicrobial resistance (AMR) poses a critical threat to food safety, particularly in African food systems where surveillance infrastructure is limited and climate change is accelerating microbial adaptation. Traditional microbiological risk assessment approaches fail to capture the biological complexity of AMR emergence across food value chains. This study developed an integrated multi-omics and machine learning framework to predict AMR emergence across Nigerian food value chains under projected climate scenar…
In a study of 487 food and 192 environmental samples from three Nigerian locations, an XGBoost model combining multiple omics datasets predicted antimicrobial resistance emergence with an AUROC of 0.942, a 16.3% improvement over single-omics approaches.
The study was cross-sectional, sampling only three Nigerian locations, and the Vibrio increases are climate-scenario projections rather than observed outcomes.
Sources
- Peer-reviewedFood Research International2026-12-01
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Truvace Impact Record TRV-2026-2392, v1: “Multi-omics-driven predictive modelling of antimicrobial resistance emergence in African food value chains under climate change scenarios..” Truvace, 2026-11-06. /record/TRV-2026-2392 (accessed on the day you copy it). sha256 1df6c508a8a35158…
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