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ABSTRACT:
SUBMITTER: Trimarsanto H
PROVIDER: S-EPMC9789135 | biostudies-literature | 2022 Dec
REPOSITORIES: biostudies-literature
Trimarsanto Hidayat H Amato Roberto R Pearson Richard D RD Sutanto Edwin E Noviyanti Rintis R Trianty Leily L Marfurt Jutta J Pava Zuleima Z Echeverry Diego F DF Lopera-Mesa Tatiana M TM Montenegro Lidia M LM Tobón-Castaño Alberto A Grigg Matthew J MJ Barber Bridget B William Timothy T Anstey Nicholas M NM Getachew Sisay S Petros Beyene B Aseffa Abraham A Assefa Ashenafi A Rahim Awab G AG Chau Nguyen H NH Hien Tran T TT Alam Mohammad S MS Khan Wasif A WA Ley Benedikt B Thriemer Kamala K Wangchuck Sonam S Hamedi Yaghoob Y Adam Ishag I Liu Yaobao Y Gao Qi Q Sriprawat Kanlaya K Ferreira Marcelo U MU Laman Moses M Barry Alyssa A Mueller Ivo I Lacerda Marcus V G MVG Llanos-Cuentas Alejandro A Krudsood Srivicha S Lon Chanthap C Mohammed Rezika R Yilma Daniel D Pereira Dhelio B DB Espino Fe E J FEJ Chu Cindy S CS Vélez Iván D ID Namaik-Larp Chayadol C Villegas Maria F MF Green Justin A JA Koh Gavin G Rayner Julian C JC Drury Eleanor E Gonçalves Sónia S Simpson Victoria V Miotto Olivo O Miles Alistair A White Nicholas J NJ Nosten Francois F Kwiatkowski Dominic P DP Price Ric N RN Auburn Sarah S
Communications biology 20221223 1
Traditionally, patient travel history has been used to distinguish imported from autochthonous malaria cases, but the dormant liver stages of Plasmodium vivax confound this approach. Molecular tools offer an alternative method to identify, and map imported cases. Using machine learning approaches incorporating hierarchical fixation index and decision tree analyses applied to 799 P. vivax genomes from 21 countries, we identified 33-SNP, 50-SNP and 55-SNP barcodes (GEO33, GEO50 and GEO55), with hi ...[more]