Craft: a machine learning approach to dengue subtyping.
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| Title: | Craft: a machine learning approach to dengue subtyping. |
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| Authors: | van Zyl DJ; Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, 7600, South Africa.; Computer Science Division, Department of Mathematical Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, 7600, South Africa., Dunaiski M; Computer Science Division, Department of Mathematical Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, 7600, South Africa., Tegally H; Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, 7600, South Africa., Baxter C; Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, 7600, South Africa.; Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, 4001, South Africa., de Oliveira T; Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, 7600, South Africa.; Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, 4001, South Africa.; KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, 4001, South Africa.; Department of Global Health, University of Washington, Seattle, Washington, 98195, United States., Xavier JS; Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, 7600, South Africa.; Institute of Agricultural Sciences, Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM), Unaí, 39100-000, Brazil.; Institute of Biological Sciences, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, 31270-901, Brazil. |
| Corporate Authors: | INFORM Africa Research Study Group |
| Source: | Bioinformatics advances [Bioinform Adv] 2025 Oct 06; Vol. 5 (1), pp. vbaf224. Date of Electronic Publication: 2025 Oct 06 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Oxford University Press Country of Publication: England NLM ID: 9918282081306676 Publication Model: eCollection Cited Medium: Internet ISSN: 2635-0041 (Electronic) Linking ISSN: 26350041 NLM ISO Abbreviation: Bioinform Adv Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2635-0041 |
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| DOI: | 10.1093/bioadv/vbaf224 |
