Geographical classification of malaria parasites through applying machine learning to whole genome sequence data
Abstract Malaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing (WGS) of Plasmodium falciparum and Plasmodium vivax genomes is providing insights into parasite genetic diversity, transmission patterns, and can inform decision making for clinical and surv...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-25568-6 |
| _version_ | 1811204211936854016 |
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| author | Wouter Deelder Emilia Manko Jody E. Phelan Susana Campino Luigi Palla Taane G. Clark |
| author_facet | Wouter Deelder Emilia Manko Jody E. Phelan Susana Campino Luigi Palla Taane G. Clark |
| author_sort | Wouter Deelder |
| collection | DOAJ |
| description | Abstract Malaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing (WGS) of Plasmodium falciparum and Plasmodium vivax genomes is providing insights into parasite genetic diversity, transmission patterns, and can inform decision making for clinical and surveillance purposes. Advances in sequencing technologies are helping to generate timely and big genomic datasets, with the prospect of applying Artificial Intelligence analytical techniques (e.g., machine learning) to support programmatic malaria control and elimination. Here, we assess the potential of applying deep learning convolutional neural network approaches to predict the geographic origin of infections (continents, countries, GPS locations) using WGS data of P. falciparum (n = 5957; 27 countries) and P. vivax (n = 659; 13 countries) isolates. Using identified high-quality genome-wide single nucleotide polymorphisms (SNPs) (P. falciparum: 750 k, P. vivax: 588 k), an analysis of population structure and ancestry revealed clustering at the country-level. When predicting locations for both species, classification (compared to regression) methods had the lowest distance errors, and > 90% accuracy at a country level. Our work demonstrates the utility of machine learning approaches for geo-classification of malaria parasites. With timelier WGS data generation across more malaria-affected regions, the performance of machine learning approaches for geo-classification will improve, thereby supporting disease control activities. |
| first_indexed | 2024-04-12T03:07:32Z |
| format | Article |
| id | doaj.art-ba09c2c1f0ed4880866bf3b0398756af |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T03:07:32Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-ba09c2c1f0ed4880866bf3b0398756af2022-12-22T03:50:27ZengNature PortfolioScientific Reports2045-23222022-12-0112111010.1038/s41598-022-25568-6Geographical classification of malaria parasites through applying machine learning to whole genome sequence dataWouter Deelder0Emilia Manko1Jody E. Phelan2Susana Campino3Luigi Palla4Taane G. Clark5London School of Hygiene & Tropical MedicineLondon School of Hygiene & Tropical MedicineLondon School of Hygiene & Tropical MedicineLondon School of Hygiene & Tropical MedicineLondon School of Hygiene & Tropical MedicineLondon School of Hygiene & Tropical MedicineAbstract Malaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing (WGS) of Plasmodium falciparum and Plasmodium vivax genomes is providing insights into parasite genetic diversity, transmission patterns, and can inform decision making for clinical and surveillance purposes. Advances in sequencing technologies are helping to generate timely and big genomic datasets, with the prospect of applying Artificial Intelligence analytical techniques (e.g., machine learning) to support programmatic malaria control and elimination. Here, we assess the potential of applying deep learning convolutional neural network approaches to predict the geographic origin of infections (continents, countries, GPS locations) using WGS data of P. falciparum (n = 5957; 27 countries) and P. vivax (n = 659; 13 countries) isolates. Using identified high-quality genome-wide single nucleotide polymorphisms (SNPs) (P. falciparum: 750 k, P. vivax: 588 k), an analysis of population structure and ancestry revealed clustering at the country-level. When predicting locations for both species, classification (compared to regression) methods had the lowest distance errors, and > 90% accuracy at a country level. Our work demonstrates the utility of machine learning approaches for geo-classification of malaria parasites. With timelier WGS data generation across more malaria-affected regions, the performance of machine learning approaches for geo-classification will improve, thereby supporting disease control activities.https://doi.org/10.1038/s41598-022-25568-6 |
| spellingShingle | Wouter Deelder Emilia Manko Jody E. Phelan Susana Campino Luigi Palla Taane G. Clark Geographical classification of malaria parasites through applying machine learning to whole genome sequence data Scientific Reports |
| title | Geographical classification of malaria parasites through applying machine learning to whole genome sequence data |
| title_full | Geographical classification of malaria parasites through applying machine learning to whole genome sequence data |
| title_fullStr | Geographical classification of malaria parasites through applying machine learning to whole genome sequence data |
| title_full_unstemmed | Geographical classification of malaria parasites through applying machine learning to whole genome sequence data |
| title_short | Geographical classification of malaria parasites through applying machine learning to whole genome sequence data |
| title_sort | geographical classification of malaria parasites through applying machine learning to whole genome sequence data |
| url | https://doi.org/10.1038/s41598-022-25568-6 |
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