Craft: A Machine Learning Approach to Dengue Subtyping.

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Bibliographic Details
Title: Craft: A Machine Learning Approach to Dengue Subtyping.
Authors: van Zyl DJ; Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch University,South Africa.; Computer Science Division, Department of Mathematical Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, South Africa., Dunaiski M; Computer Science Division, Department of Mathematical Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, South Africa., Tegally H; Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch University,South Africa., Baxter C; Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch University,South Africa.; Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa., de Oliveira T; Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch University,South Africa.; Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa.; KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa.; Department of Global Health, University of Washington; Seattle, USA., Xavier JS; Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch University,South Africa.; Institute of Agricultural Sciences, Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM), Unaí, Brazil.; Institute of Biological Sciences, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil.
Corporate Authors: INFORM Africa research study group
Source: BioRxiv : the preprint server for biology [bioRxiv] 2025 Feb 13. Date of Electronic Publication: 2025 Feb 13.
Publication Type: Journal Article; Preprint
Journal Info: Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2692-8205
DOI:10.1101/2025.02.10.637410