Craft: A Machine Learning Approach to Dengue Subtyping
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
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 ( C haos Ra ndom F ores t ), 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.
Contact
danielvanzyl@sun.ac.za
Supplementary information
A supplemental table acknowledging the authors of the GISAID dengue sequences is available at Bioinformatics online.
