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Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper.

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Title: Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper.
Authors: Orcales F; Department of Biology, San Francisco State University, San Francisco, California, United States of America.; University of California San Francisco, San Francisco, California, United States of America., Moctezuma Tan L; Department of Biology, San Francisco State University, San Francisco, California, United States of America.; Department of Statistics, California State University East Bay, Hayward, California, United States of America., Johnson-Hagler M; Department of Biology, San Francisco State University, San Francisco, California, United States of America., Suntay JM; Department of Biology, San Francisco State University, San Francisco, California, United States of America.; University of California San Francisco, San Francisco, California, United States of America., Ali J; Department of Biology, San Francisco State University, San Francisco, California, United States of America., Recto K; Department of Biology, San Francisco State University, San Francisco, California, United States of America., Glenn P; Department of Biology, San Francisco State University, San Francisco, California, United States of America.; David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America., Pennings P; Department of Biology, San Francisco State University, San Francisco, California, United States of America.
Source: PLoS computational biology [PLoS Comput Biol] 2024 Dec 30; Vol. 20 (12), pp. e1012579. Date of Electronic Publication: 2024 Dec 30 (Print Publication: 2024).
Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Orcales+F%22">Orcales F</searchLink>; Department of Biology, San Francisco State University, San Francisco, California, United States of America.; University of California San Francisco, San Francisco, California, United States of America.<br /><searchLink fieldCode="AU" term="%22Moctezuma+Tan+L%22">Moctezuma Tan L</searchLink>; Department of Biology, San Francisco State University, San Francisco, California, United States of America.; Department of Statistics, California State University East Bay, Hayward, California, United States of America.<br /><searchLink fieldCode="AU" term="%22Johnson-Hagler+M%22">Johnson-Hagler M</searchLink>; Department of Biology, San Francisco State University, San Francisco, California, United States of America.<br /><searchLink fieldCode="AU" term="%22Suntay+JM%22">Suntay JM</searchLink>; Department of Biology, San Francisco State University, San Francisco, California, United States of America.; University of California San Francisco, San Francisco, California, United States of America.<br /><searchLink fieldCode="AU" term="%22Ali+J%22">Ali J</searchLink>; Department of Biology, San Francisco State University, San Francisco, California, United States of America.<br /><searchLink fieldCode="AU" term="%22Recto+K%22">Recto K</searchLink>; Department of Biology, San Francisco State University, San Francisco, California, United States of America.<br /><searchLink fieldCode="AU" term="%22Glenn+P%22">Glenn P</searchLink>; Department of Biology, San Francisco State University, San Francisco, California, United States of America.; David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America.<br /><searchLink fieldCode="AU" term="%22Pennings+P%22">Pennings P</searchLink>; Department of Biology, San Francisco State University, San Francisco, California, United States of America.
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              Text: 2024 Dec 30
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