Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

The use of Raman spectroscopy for pathogen identification is hampered by the weak Raman signal and phenotypic diversity of bacterial cells. Here the authors generate an extensive dataset of bacterial Raman spectra and apply deep learning to identify common bacterial pathogens and predict antibiotic...

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Autores principales:	Chi-Sing Ho, Neal Jean, Catherine A. Hogan, Lena Blackmon, Stefanie S. Jeffrey, Mark Holodniy, Niaz Banaei, Amr A. E. Saleh, Stefano Ermon, Jennifer Dionne
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/ee4336f25b654cc5b76c1a698f533e71
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spelling	oai:doaj.org-article:ee4336f25b654cc5b76c1a698f533e712021-12-02T15:35:14ZRapid identification of pathogenic bacteria using Raman spectroscopy and deep learning10.1038/s41467-019-12898-92041-1723https://doaj.org/article/ee4336f25b654cc5b76c1a698f533e712019-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-12898-9https://doaj.org/toc/2041-1723The use of Raman spectroscopy for pathogen identification is hampered by the weak Raman signal and phenotypic diversity of bacterial cells. Here the authors generate an extensive dataset of bacterial Raman spectra and apply deep learning to identify common bacterial pathogens and predict antibiotic treatment from noisy Raman spectra.Chi-Sing HoNeal JeanCatherine A. HoganLena BlackmonStefanie S. JeffreyMark HolodniyNiaz BanaeiAmr A. E. SalehStefano ErmonJennifer DionneNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-8 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Chi-Sing Ho Neal Jean Catherine A. Hogan Lena Blackmon Stefanie S. Jeffrey Mark Holodniy Niaz Banaei Amr A. E. Saleh Stefano Ermon Jennifer Dionne Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
description	The use of Raman spectroscopy for pathogen identification is hampered by the weak Raman signal and phenotypic diversity of bacterial cells. Here the authors generate an extensive dataset of bacterial Raman spectra and apply deep learning to identify common bacterial pathogens and predict antibiotic treatment from noisy Raman spectra.
format	article
author	Chi-Sing Ho Neal Jean Catherine A. Hogan Lena Blackmon Stefanie S. Jeffrey Mark Holodniy Niaz Banaei Amr A. E. Saleh Stefano Ermon Jennifer Dionne
author_facet	Chi-Sing Ho Neal Jean Catherine A. Hogan Lena Blackmon Stefanie S. Jeffrey Mark Holodniy Niaz Banaei Amr A. E. Saleh Stefano Ermon Jennifer Dionne
author_sort	Chi-Sing Ho
title	Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
title_short	Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
title_full	Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
title_fullStr	Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
title_full_unstemmed	Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
title_sort	rapid identification of pathogenic bacteria using raman spectroscopy and deep learning
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/ee4336f25b654cc5b76c1a698f533e71
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Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

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