Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics

Abstract Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of...

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Bibliographic Details
Main Authors: Ariane Khaledi, Aaron Weimann, Monika Schniederjans, Ehsaneddin Asgari, Tzu‐Hao Kuo, Antonio Oliver, Gabriel Cabot, Axel Kola, Petra Gastmeier, Michael Hogardt, Daniel Jonas, Mohammad RK Mofrad, Andreas Bremges, Alice C McHardy, Susanne Häussler
Format: Article
Language:English
Published: Wiley 2020-03-01
Series:EMBO Molecular Medicine
Subjects:
antibiotic resistance
biomarkers
clinical isolates
machine learning
molecular diagnostics
Online Access:https://doi.org/10.15252/emmm.201910264

Internet

https://doi.org/10.15252/emmm.201910264

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