Craft: A Machine Learning Approach to Dengue Subtyping.

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Tác giả: Cheryl Baxter, Tulio de Oliveira, Marcel Dunaiski, Houriiyah Tegally, Daniel J van Zyl, Joicymara S Xavier

Ngôn ngữ: eng

Ký hiệu phân loại: 623.8256 Nautical engineering and seamanship

Thông tin xuất bản: United States : bioRxiv : the preprint server for biology , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 674031

MOTIVATION: The dengue virus poses a major global health threat, with nearly 390 million infections annually. A recently proposed hierarchical dengue nomenclature system enhances spatial resolution by defining major and minor lineages within genotypes, aiding efforts to track viral evolution. While current subtyping tools - Genome Detective, GLUE, and NextClade - rely on computationally intensive sequence alignment and phylogenetic inference, machine learning presents a promising alternative for achieving accurate and rapid classification. RESULTS: We present Craft ( CONTACT: danielvanzyl@sun.ac.za. SUPPLEMENTARY INFORMATION: A supplemental table acknowledging the authors of the GISAID dengue sequences is available at
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