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2020-09-14Zeitschriftenartikel
Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning
dc.contributor.authorPataki, Bálint Ármin
dc.contributor.authorMatamoros, Sébastien
dc.contributor.authorvan der Putten, Boas C. L.
dc.contributor.authorRemondini, Daniel
dc.contributor.authorGiampieri, Enrico
dc.contributor.authorAytan-Aktug, Derya
dc.contributor.authorHendriksen, Rene S.
dc.contributor.authorLund, Ole
dc.contributor.authorCsabai, István
dc.contributor.authorSchultsz, Constance
dc.contributor.authorSPS COMPARE ML-AMR Group
dc.date.accessioned2024-01-04T15:52:52Z
dc.date.available2024-01-04T15:52:52Z
dc.date.issued2020-09-14none
dc.identifier.other10.1038/s41598-020-71693-5
dc.identifier.urihttp://edoc.rki.de/176904/11449
dc.description.abstractIt is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes. We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of the qnrS1 gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 93% of the test samples’ MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work does not treat machine learning as a black box model concept, but also identifies the genomic features that determine susceptibility. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement.eng
dc.language.isoengnone
dc.publisherRobert Koch-Institut
dc.rights(CC BY 3.0 DE) Namensnennung 3.0 Deutschlandger
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/de/
dc.subjectcomputational biology and bioinformaticseng
dc.subjectmicrobiologyeng
dc.subject.ddc610 Medizin und Gesundheitnone
dc.titleUnderstanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learningnone
dc.typearticle
dc.identifier.urnurn:nbn:de:0257-176904/11449-6
dc.type.versionpublishedVersionnone
local.edoc.container-titlescientific reportsnone
local.edoc.container-issn2045-2322none
local.edoc.pages9none
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-urlhttps://www.nature.com/srep/none
local.edoc.container-publisher-nameSpringer Naturenone
local.edoc.container-volume10none
local.edoc.container-reportyear2020none
dc.description.versionPeer Reviewednone

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