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. |
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| 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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| ISSN: | 1553-7358 |
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| DOI: | 10.1371/journal.pcbi.1012579 |
