A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections

Diagnosing acute infections based on transcriptional host response shows promise, but generalizability is wanting. Here, the authors use a co-normalization framework to train a classifier to diagnose acute infections and apply it to independent data on a targeted diagnostic platform.

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Autores principales:	Michael B. Mayhew, Ljubomir Buturovic, Roland Luethy, Uros Midic, Andrew R. Moore, Jonasel A. Roque, Brian D. Shaller, Tola Asuni, David Rawling, Melissa Remmel, Kirindi Choi, James Wacker, Purvesh Khatri, Angela J. Rogers, Timothy E. Sweeney
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/1514bf7fdf6d4f5db8c9b6afeda1ce2b
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spelling	oai:doaj.org-article:1514bf7fdf6d4f5db8c9b6afeda1ce2b2021-12-02T16:49:29ZA generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections10.1038/s41467-020-14975-w2041-1723https://doaj.org/article/1514bf7fdf6d4f5db8c9b6afeda1ce2b2020-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-14975-whttps://doaj.org/toc/2041-1723Diagnosing acute infections based on transcriptional host response shows promise, but generalizability is wanting. Here, the authors use a co-normalization framework to train a classifier to diagnose acute infections and apply it to independent data on a targeted diagnostic platform.Michael B. MayhewLjubomir ButurovicRoland LuethyUros MidicAndrew R. MooreJonasel A. RoqueBrian D. ShallerTola AsuniDavid RawlingMelissa RemmelKirindi ChoiJames WackerPurvesh KhatriAngela J. RogersTimothy E. SweeneyNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Michael B. Mayhew Ljubomir Buturovic Roland Luethy Uros Midic Andrew R. Moore Jonasel A. Roque Brian D. Shaller Tola Asuni David Rawling Melissa Remmel Kirindi Choi James Wacker Purvesh Khatri Angela J. Rogers Timothy E. Sweeney A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections
description	Diagnosing acute infections based on transcriptional host response shows promise, but generalizability is wanting. Here, the authors use a co-normalization framework to train a classifier to diagnose acute infections and apply it to independent data on a targeted diagnostic platform.
format	article
author	Michael B. Mayhew Ljubomir Buturovic Roland Luethy Uros Midic Andrew R. Moore Jonasel A. Roque Brian D. Shaller Tola Asuni David Rawling Melissa Remmel Kirindi Choi James Wacker Purvesh Khatri Angela J. Rogers Timothy E. Sweeney
author_facet	Michael B. Mayhew Ljubomir Buturovic Roland Luethy Uros Midic Andrew R. Moore Jonasel A. Roque Brian D. Shaller Tola Asuni David Rawling Melissa Remmel Kirindi Choi James Wacker Purvesh Khatri Angela J. Rogers Timothy E. Sweeney
author_sort	Michael B. Mayhew
title	A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections
title_short	A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections
title_full	A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections
title_fullStr	A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections
title_full_unstemmed	A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections
title_sort	generalizable 29-mrna neural-network classifier for acute bacterial and viral infections
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/1514bf7fdf6d4f5db8c9b6afeda1ce2b
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A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections

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