Related Experiment Video
Updated: Jan 6, 2026

Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes
Published on: March 3, 2023
Balancing complexity and clarity-towards clinician-ready antibiotic resistance prediction models
1Department of Medical Microbiology, College of Health Sciences, Makerere University (MakCHS), Kampala, 7072, Uganda.
Motivation:
The escalating challenge of antibiotic resistance (ABR) demands clinician-ready machine learning models that are not only accurate but interpretable.
Results:
By treating resistance genes as independent features and augmenting them with curated single-nucleotide polymorphisms and contextual markers, this approach delivers scalable, transparent predictions aligned with clinical decision-making needs.
Availability And Implementation:
Not applicable.
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