Learning From Limited Data: Towards Best Practice Techniques for Antimicrobial Resistance Prediction From Whole Genome Sequencing Data

Antimicrobial resistance prediction from whole genome sequencing data (WGS) is an emerging application of machine learning, promising to improve antimicrobial resistance surveillance and outbreak monitoring. Despite significant reductions in sequencing cost, the availability and sampling diversity o...

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Detalles Bibliográficos
Autores principales: Lüftinger, Lukas, Májek, Peter, Beisken, Stephan, Rattei, Thomas, Posch, Andreas E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Cellular and Infection Microbiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7917081/
https://www.ncbi.nlm.nih.gov/pubmed/33659219
http://dx.doi.org/10.3389/fcimb.2021.610348

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7917081/
https://www.ncbi.nlm.nih.gov/pubmed/33659219
http://dx.doi.org/10.3389/fcimb.2021.610348

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