Cargando…
Predicting Antimicrobial Resistance Using Partial Genome Alignments
Antimicrobial resistance (AMR) is an important global health threat that impacts millions of people worldwide each year. Developing methods that can detect and predict AMR phenotypes can help to mitigate the spread of AMR by informing clinical decision making and appropriate mitigation strategies. M...
Autores principales: | Aytan-Aktug, D., Nguyen, M., Clausen, P. T. L. C., Stevens, R. L., Aarestrup, F. M., Lund, O., Davis, J. J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269213/ https://www.ncbi.nlm.nih.gov/pubmed/34128695 http://dx.doi.org/10.1128/mSystems.00185-21 |
Ejemplares similares
-
Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks
por: Aytan-Aktug, D., et al.
Publicado: (2020) -
ResFinder – an open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes
por: Florensa, Alfred Ferrer, et al.
Publicado: (2022) -
PlasmidHostFinder: Prediction of Plasmid Hosts Using Random Forest
por: Aytan-Aktug, Derya, et al.
Publicado: (2022) -
SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies
por: Aytan-Aktug, Derya, et al.
Publicado: (2022) -
Rapid and precise alignment of raw reads against redundant databases with KMA
por: Clausen, Philip T. L. C., et al.
Publicado: (2018)