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

Title
A cautionary note on the use of unsupervised machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices
Authors
Keywords
Malarial parasites, Malaria, Machine learning algorithms, Genetic epidemiology, Genetics, Population genetics, DNA recombination, Plasmodium
Journal
PLoS Genetics
Volume 16, Issue 10, Pages e1009037
Publisher
Public Library of Science (PLoS)
Online
2020-10-10
DOI
10.1371/journal.pgen.1009037

Ask authors/readers for more resources

Reprint

Contact the author

Find Funding. Review Successful Grants.

Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.

Explore

Create your own webinar

Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.

Create Now