Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes

Predicting bacterial growth from genome sequences is important for a rapid characterization of strains in clinical diagnostics and to disclose candidate novel targets for anti-infective drugs. Previous studies have dissected the relationship between bacterial growth and genotype in mutant libraries...

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Autores principales:	Sam Benkwitz-Bedford, Martin Palm, Talip Yasir Demirtas, Ville Mustonen, Anne Farewell, Jonas Warringer, Leopold Parts, Danesh Moradigaravand
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2021
Materias:	Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/72c006c5dc5a4b63803341eca1930dc6
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spelling	oai:doaj.org-article:72c006c5dc5a4b63803341eca1930dc62021-12-02T19:27:35ZMachine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes2379-507710.1128/mSystems.00346-21https://doaj.org/article/72c006c5dc5a4b63803341eca1930dc62021-08-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00346-21https://doaj.org/toc/2379-5077 Predicting bacterial growth from genome sequences is important for a rapid characterization of strains in clinical diagnostics and to disclose candidate novel targets for anti-infective drugs. Previous studies have dissected the relationship between bacterial growth and genotype in mutant libraries for laboratory strains, yet no study so far has examined the predictive power of genome sequence in natural strains.Sam Benkwitz-BedfordMartin PalmTalip Yasir DemirtasVille MustonenAnne FarewellJonas WarringerLeopold PartsDanesh MoradigaravandAmerican Society for MicrobiologyarticleMicrobiologyQR1-502ENmSystems, Vol 6, Iss 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Microbiology QR1-502
spellingShingle	Microbiology QR1-502 Sam Benkwitz-Bedford Martin Palm Talip Yasir Demirtas Ville Mustonen Anne Farewell Jonas Warringer Leopold Parts Danesh Moradigaravand Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes
description	Predicting bacterial growth from genome sequences is important for a rapid characterization of strains in clinical diagnostics and to disclose candidate novel targets for anti-infective drugs. Previous studies have dissected the relationship between bacterial growth and genotype in mutant libraries for laboratory strains, yet no study so far has examined the predictive power of genome sequence in natural strains.
format	article
author	Sam Benkwitz-Bedford Martin Palm Talip Yasir Demirtas Ville Mustonen Anne Farewell Jonas Warringer Leopold Parts Danesh Moradigaravand
author_facet	Sam Benkwitz-Bedford Martin Palm Talip Yasir Demirtas Ville Mustonen Anne Farewell Jonas Warringer Leopold Parts Danesh Moradigaravand
author_sort	Sam Benkwitz-Bedford
title	Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes
title_short	Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes
title_full	Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes
title_fullStr	Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes
title_full_unstemmed	Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes
title_sort	machine learning prediction of resistance to subinhibitory antimicrobial concentrations from escherichia coli genomes
publisher	American Society for Microbiology
publishDate	2021
url	https://doaj.org/article/72c006c5dc5a4b63803341eca1930dc6
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Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes

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