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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 <jats:italic>bacitracin<\/jats:italic> and <jats:italic>vancomycin<\/jats:italic>. Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies.<\/jats:p>","DOI":"10.1038\/s41598-020-67949-9","type":"journal-article","created":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T10:02:57Z","timestamp":1593770577000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["PARGT: a software tool for predicting antimicrobial resistance in bacteria"],"prefix":"10.1038","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3454-5861","authenticated-orcid":false,"given":"Abu Sayed","family":"Chowdhury","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douglas R.","family":"Call","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shira L.","family":"Broschat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,3]]},"reference":[{"key":"67949_CR1","doi-asserted-by":"crossref","unstructured":"Boolchandani, M., D\u2019Souza, A. 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