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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Veröffentlicht in:	PLoS genetics 2020-10, Vol.16 (10), p.e1009037-e1009037
Hauptverfasser:	Watson, James A, Taylor, Aimee R, Ashley, Elizabeth A, Dondorp, Arjen, Buckee, Caroline O, White, Nicholas J, Holmes, Chris C
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Antimalarials - therapeutic use Biology and Life Sciences Cambodia - epidemiology Cluster Analysis Clustering Computer and Information Sciences Distribution Drug Resistance - genetics Epidemiology Genetic analysis Genetic aspects Genetic distance Genetics, Population - statistics & numerical data Genomes Genotype Haplotypes Humans Identification and classification Learning Learning algorithms Machine learning Malaria Malaria, Falciparum - drug therapy Malaria, Falciparum - epidemiology Malaria, Falciparum - genetics Malaria, Falciparum - parasitology Mathematical models Medicine Medicine and Health Sciences Microbial populations Molecular Epidemiology Parasites Physical Sciences Plasmodium falciparum Plasmodium falciparum - genetics Plasmodium falciparum - pathogenicity Population genetics Population structure Principal components analysis Public health Research and Analysis Methods Safety and security measures Sensitivity analysis Sentinel health events Software Statistical analysis Structure Supervision Unsupervised Machine Learning
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