A biochemically-interpretable machine learning classifier for microbial GWAS

Current machine learning classifiers have been applied to whole-genome sequencing data to identify determinants of antimicrobial resistance, but they lack interpretability. Here the authors present a metabolic machine learning classifier that uses flux balance analysis to estimate the biochemical ef...

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Autores principales:	Erol S. Kavvas, Laurence Yang, Jonathan M. Monk, David Heckmann, Bernhard O. Palsson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/975d3eec652849ec8a3f9bfe2f5ad9c0
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id	oai:doaj.org-article:975d3eec652849ec8a3f9bfe2f5ad9c0
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spelling	oai:doaj.org-article:975d3eec652849ec8a3f9bfe2f5ad9c02021-12-02T15:45:14ZA biochemically-interpretable machine learning classifier for microbial GWAS10.1038/s41467-020-16310-92041-1723https://doaj.org/article/975d3eec652849ec8a3f9bfe2f5ad9c02020-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-16310-9https://doaj.org/toc/2041-1723Current machine learning classifiers have been applied to whole-genome sequencing data to identify determinants of antimicrobial resistance, but they lack interpretability. Here the authors present a metabolic machine learning classifier that uses flux balance analysis to estimate the biochemical effects of alleles.Erol S. KavvasLaurence YangJonathan M. MonkDavid HeckmannBernhard O. PalssonNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Erol S. Kavvas Laurence Yang Jonathan M. Monk David Heckmann Bernhard O. Palsson A biochemically-interpretable machine learning classifier for microbial GWAS
description	Current machine learning classifiers have been applied to whole-genome sequencing data to identify determinants of antimicrobial resistance, but they lack interpretability. Here the authors present a metabolic machine learning classifier that uses flux balance analysis to estimate the biochemical effects of alleles.
format	article
author	Erol S. Kavvas Laurence Yang Jonathan M. Monk David Heckmann Bernhard O. Palsson
author_facet	Erol S. Kavvas Laurence Yang Jonathan M. Monk David Heckmann Bernhard O. Palsson
author_sort	Erol S. Kavvas
title	A biochemically-interpretable machine learning classifier for microbial GWAS
title_short	A biochemically-interpretable machine learning classifier for microbial GWAS
title_full	A biochemically-interpretable machine learning classifier for microbial GWAS
title_fullStr	A biochemically-interpretable machine learning classifier for microbial GWAS
title_full_unstemmed	A biochemically-interpretable machine learning classifier for microbial GWAS
title_sort	biochemically-interpretable machine learning classifier for microbial gwas
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/975d3eec652849ec8a3f9bfe2f5ad9c0
work_keys_str_mv	AT erolskavvas abiochemicallyinterpretablemachinelearningclassifierformicrobialgwas AT laurenceyang abiochemicallyinterpretablemachinelearningclassifierformicrobialgwas AT jonathanmmonk abiochemicallyinterpretablemachinelearningclassifierformicrobialgwas AT davidheckmann abiochemicallyinterpretablemachinelearningclassifierformicrobialgwas AT bernhardopalsson abiochemicallyinterpretablemachinelearningclassifierformicrobialgwas AT erolskavvas biochemicallyinterpretablemachinelearningclassifierformicrobialgwas AT laurenceyang biochemicallyinterpretablemachinelearningclassifierformicrobialgwas AT jonathanmmonk biochemicallyinterpretablemachinelearningclassifierformicrobialgwas AT davidheckmann biochemicallyinterpretablemachinelearningclassifierformicrobialgwas AT bernhardopalsson biochemicallyinterpretablemachinelearningclassifierformicrobialgwas
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A biochemically-interpretable machine learning classifier for microbial GWAS

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