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

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 414 drug‐...

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Detalles Bibliográficos
Autores principales: Khaledi, Ariane, Weimann, Aaron, Schniederjans, Monika, Asgari, Ehsaneddin, Kuo, Tzu‐Hao, Oliver, Antonio, Cabot, Gabriel, Kola, Axel, Gastmeier, Petra, Hogardt, Michael, Jonas, Daniel, Mofrad, Mohammad RK, Bremges, Andreas, McHardy, Alice C, Häussler, Susanne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7059009/
https://www.ncbi.nlm.nih.gov/pubmed/32048461
http://dx.doi.org/10.15252/emmm.201910264

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7059009/
https://www.ncbi.nlm.nih.gov/pubmed/32048461
http://dx.doi.org/10.15252/emmm.201910264

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