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Craft: a machine learning approach to dengue subtyping.

Daniel J van Zyl1,2, Marcel Dunaiski2, Houriiyah Tegally1

  • 1Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, 7600, South Africa.

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Summary
This summary is machine-generated.

A new machine learning framework, Craft, offers rapid and accurate dengue virus subtyping. It achieves high precision, classifying over 140,000 sequences per minute, outperforming existing methods for tracking viral evolution.

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Area of Science:

  • Virology
  • Bioinformatics
  • Machine Learning

Background:

  • Dengue virus causes nearly 390 million infections annually, necessitating effective tracking of its evolution.
  • A hierarchical nomenclature system improves spatial resolution for dengue virus lineage classification.
  • Current subtyping tools are computationally intensive, relying on sequence alignment and phylogenetic inference.

Purpose of the Study:

  • To introduce Craft (Chaos Random Forest), a machine learning framework for rapid and accurate dengue virus subtyping.
  • To evaluate Craft's performance against existing dengue subtyping tools in terms of speed and accuracy.

Main Methods:

  • Development of the Craft machine learning framework.
  • Benchmarking Craft against Genome Detective, GLUE, and Nextclade using a consensus-based test set.
  • Assessment of classification accuracy and speed, including performance on short sequence segments.

Main Results:

  • Craft achieves 99.5% accuracy on a hold-out test set.
  • Craft classifies over 140,000 sequences per minute, significantly faster than existing tools.
  • Craft maintains high accuracy even with sequence segments as short as 700 nucleotides.

Conclusions:

  • Craft provides a computationally efficient and highly accurate alternative for dengue virus subtyping.
  • The framework aids in tracking dengue virus evolution and lineage classification.
  • Craft's speed and accuracy make it a valuable tool for global health surveillance of dengue.