A cautionary note on the use of unsupervised machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices

Genetic surveillance of malaria parasites supports malaria control programmes, treatment guidelines and elimination strategies. Surveillance studies often pose questions about malaria parasite ancestry (e.g. how antimalarial resistance has spread) and employ statistical methods that characterise par...

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Detalles Bibliográficos
Autores principales: Watson, James A., Taylor, Aimee R., Ashley, Elizabeth A., Dondorp, Arjen, Buckee, Caroline O., White, Nicholas J., Holmes, Chris C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577480/
https://www.ncbi.nlm.nih.gov/pubmed/33035220
http://dx.doi.org/10.1371/journal.pgen.1009037

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577480/
https://www.ncbi.nlm.nih.gov/pubmed/33035220
http://dx.doi.org/10.1371/journal.pgen.1009037

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