Craft: a machine learning approach to dengue subtyping.

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 (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 exi

SUBMITTER: van Zyl DJ 

PROVIDER: S-EPMC12527244 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature