Machine learning and phylogenetic analysis allow for predicting antibiotic resistance inM. tuberculosis
Alper Yurtseven,
Sofia Buyanova,
Amay Ajaykumar Agrawal
et al.
Abstract:Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attem… Show more
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