TB-ML—a framework for comparing machine learning approaches to predict drug resistance of Mycobacterium tuberculosis

MOTIVATION: Machine learning (ML) has shown impressive performance in predicting antimicrobial resistance (AMR) from sequence data, including for Mycobacterium tuberculosis, the causative agent of tuberculosis. However, current ML development and publication practices make it difficult for researche...

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Detalles Bibliográficos
Autores principales: Libiseller-Egger, Julian, Wang, Linfeng, Deelder, Wouter, Campino, Susana, Clark, Taane G, Phelan, Jody E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Application Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10074023/
https://www.ncbi.nlm.nih.gov/pubmed/37033466
http://dx.doi.org/10.1093/bioadv/vbad040

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10074023/
https://www.ncbi.nlm.nih.gov/pubmed/37033466
http://dx.doi.org/10.1093/bioadv/vbad040

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