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Machine learning with random subspace ensembles identifies antimicrobial resistance determinants from pan-genomes of three pathogens
The evolution of antimicrobial resistance (AMR) poses a persistent threat to global public health. Sequencing efforts have already yielded genome sequences for thousands of resistant microbial isolates and require robust computational tools to systematically elucidate the genetic basis for AMR. Here...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067475/ https://www.ncbi.nlm.nih.gov/pubmed/32119670 http://dx.doi.org/10.1371/journal.pcbi.1007608 |