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dc.contributor.authorKhaledi, Ariane
dc.contributor.authorWeimann, Aaron
dc.contributor.authorSchniederjans, Monika
dc.contributor.authorAsgari, Ehsaneddin
dc.contributor.authorKuo, Tzu‐Hao
dc.contributor.authorOliver, Antonio
dc.contributor.authorCabot, Gabriel
dc.contributor.authorKola, Axel
dc.contributor.authorGastmeier, Petra
dc.contributor.authorHogardt, Michael
dc.contributor.authorJonas, Daniel
dc.contributor.authorMofrad, Mohammad RK
dc.contributor.authorBremges, Andreas
dc.contributor.authorMcHardy, Alice C
dc.contributor.authorHäussler, Susanne
dc.date.accessioned2022-06-13T09:55:49Z
dc.date.available2022-06-13T09:55:49Z
dc.date.issued2020-02-12
dc.date.submitted2019-01-02
dc.identifier.issn1757-4676
dc.identifier.doi10.15252/emmm.201910264
dc.identifier.urihttp://hdl.handle.net/10033/623211
dc.description.abstractLimited 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-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and expression profiles, we generated predictive models and identified biomarkers of resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8–0.9) or very high (> 0.9) sensitivity and predictive values. For all drugs except for ciprofloxacin, gene expression information improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.en_US
dc.description.sponsorshipFP7 Ideas: European Research Councilen_US
dc.language.isoenen_US
dc.publisherEMBOen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMolecular Medicineen_US
dc.titlePredicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnosticsen_US
dc.typeArticleen_US
dc.identifier.eissn1757-4684
dc.identifier.journalEMBO Molecular Medicineen_US
dc.identifier.pii10.15252/emmm.201910264
dc.source.volume12
dc.source.issue3
refterms.dateFOA2022-06-13T09:55:50Z
dc.source.journaltitleEMBO Molecular Medicine


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International