PARGT: a software tool for predicting antimicrobial resistance in bacteria

Abstract With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in t...

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Autores principales:	Abu Sayed Chowdhury, Douglas R. Call, Shira L. Broschat
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fecbf25a3723448bb1b91406076c2eaf
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spelling	oai:doaj.org-article:fecbf25a3723448bb1b91406076c2eaf2021-12-02T18:18:51ZPARGT: a software tool for predicting antimicrobial resistance in bacteria10.1038/s41598-020-67949-92045-2322https://doaj.org/article/fecbf25a3723448bb1b91406076c2eaf2020-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-67949-9https://doaj.org/toc/2045-2322Abstract With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in the lab. In previous work, we proposed a game-theory-based feature evaluation algorithm. When using the protein characteristics identified by this algorithm, called ‘features’ in machine learning, our model accurately identified antimicrobial resistance (AMR) genes in Gram-negative bacteria. Here we extend our study to Gram-positive bacteria showing that coupling game-theory-identified features with machine learning achieved classification accuracies between 87% and 90% for genes encoding resistance to the antibiotics bacitracin and vancomycin. Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies.Abu Sayed ChowdhuryDouglas R. CallShira L. BroschatNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-7 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Abu Sayed Chowdhury Douglas R. Call Shira L. Broschat PARGT: a software tool for predicting antimicrobial resistance in bacteria
description	Abstract With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in the lab. In previous work, we proposed a game-theory-based feature evaluation algorithm. When using the protein characteristics identified by this algorithm, called ‘features’ in machine learning, our model accurately identified antimicrobial resistance (AMR) genes in Gram-negative bacteria. Here we extend our study to Gram-positive bacteria showing that coupling game-theory-identified features with machine learning achieved classification accuracies between 87% and 90% for genes encoding resistance to the antibiotics bacitracin and vancomycin. Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies.
format	article
author	Abu Sayed Chowdhury Douglas R. Call Shira L. Broschat
author_facet	Abu Sayed Chowdhury Douglas R. Call Shira L. Broschat
author_sort	Abu Sayed Chowdhury
title	PARGT: a software tool for predicting antimicrobial resistance in bacteria
title_short	PARGT: a software tool for predicting antimicrobial resistance in bacteria
title_full	PARGT: a software tool for predicting antimicrobial resistance in bacteria
title_fullStr	PARGT: a software tool for predicting antimicrobial resistance in bacteria
title_full_unstemmed	PARGT: a software tool for predicting antimicrobial resistance in bacteria
title_sort	pargt: a software tool for predicting antimicrobial resistance in bacteria
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/fecbf25a3723448bb1b91406076c2eaf
work_keys_str_mv	AT abusayedchowdhury pargtasoftwaretoolforpredictingantimicrobialresistanceinbacteria AT douglasrcall pargtasoftwaretoolforpredictingantimicrobialresistanceinbacteria AT shiralbroschat pargtasoftwaretoolforpredictingantimicrobialresistanceinbacteria
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PARGT: a software tool for predicting antimicrobial resistance in bacteria

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