Craft: A Machine Learning Approach to Dengue Subtyping

van Zyl, D. J.; Dunaiski, M.; Tegally, H.; Baxter, C.; de Oliveira, T.; Xavier, J. S.; The INFORM Africa research study group,

doi:10.1101/2025.02.10.637410

bioRxiv · 10.1101/2025.02.10.637410

Craft: A Machine Learning Approach to Dengue Subtyping

van Zyl, D. J.·Dunaiski, M.·Tegally, H.·Baxter, C.·de Oliveira, T.·Xavier, J. S.·The INFORM Africa research study group,

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Abstract

MotivationThe 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. ResultsWe present Craft (Chaos Random Forest), a machine learning framework for dengue subtyping. We demonstrate that Craft is capable of faster classification speeds while matching or surpassing the accuracy of existing tools. Craft achieves 99.5% accuracy on a hold-out test set and processes over 140 000 sequences per minute. Notably, Craft maintains remarkably high accuracy even when classifying sequence segments as short as 700 nucleotides. Contactdanielvanzyl@sun.ac.za Supplementary informationA supplemental table acknowledging the authors of the GISAID dengue sequences is available at Bioinformatics online.

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van Zyl, D. J., Dunaiski, M., Tegally, H., Baxter, C., de Oliveira, T., Xavier, J. S., The INFORM Africa research study group,. 2025-02-13. Craft: A Machine Learning Approach to Dengue Subtyping. https://doi.org/10.1101/2025.02.10.637410

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