A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria

An online, amendable classifier for detecting malaria parasite country of origin from genomic data is developed using a machine learning approach.

Bibliographic Details
Main Authors:	Hidayat Trimarsanto, Roberto Amato, Richard D. Pearson, Edwin Sutanto, Rintis Noviyanti, Leily Trianty, Jutta Marfurt, Zuleima Pava, Diego F. Echeverry, Tatiana M. Lopera-Mesa, Lidia M. Montenegro, Alberto Tobón-Castaño, Matthew J. Grigg, Bridget Barber, Timothy William, Nicholas M. Anstey, Sisay Getachew, Beyene Petros, Abraham Aseffa, Ashenafi Assefa, Awab G. Rahim, Nguyen H. Chau, Tran T. Hien, Mohammad S. Alam, Wasif A. Khan, Benedikt Ley, Kamala Thriemer, Sonam Wangchuck, Yaghoob Hamedi, Ishag Adam, Yaobao Liu, Qi Gao, Kanlaya Sriprawat, Marcelo U. Ferreira, Moses Laman, Alyssa Barry, Ivo Mueller, Marcus V. G. Lacerda, Alejandro Llanos-Cuentas, Srivicha Krudsood, Chanthap Lon, Rezika Mohammed, Daniel Yilma, Dhelio B. Pereira, Fe E. J. Espino, Cindy S. Chu, Iván D. Vélez, Chayadol Namaik-larp, Maria F. Villegas, Justin A. Green, Gavin Koh, Julian C. Rayner, Eleanor Drury, Sónia Gonçalves, Victoria Simpson, Olivo Miotto, Alistair Miles, Nicholas J. White, Francois Nosten, Dominic P. Kwiatkowski, Ric N. Price, Sarah Auburn
Format:	Article
Language:	English
Published:	Nature Portfolio 2022-12-01
Series:	Communications Biology
Online Access:	https://doi.org/10.1038/s42003-022-04352-2

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