Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN

Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic sequencing data often achieve varying results: high...

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Detalles Bibliográficos
Autores principales: Kuang, Xingyan, Wang, Fan, Hernandez, Kyle M., Zhang, Zhenyu, Grossman, Robert L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844416/
https://www.ncbi.nlm.nih.gov/pubmed/35165358
http://dx.doi.org/10.1038/s41598-022-06449-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844416/
https://www.ncbi.nlm.nih.gov/pubmed/35165358
http://dx.doi.org/10.1038/s41598-022-06449-4

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