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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 University,South Africa.

Biorxiv : the Preprint Server for Biology
|February 24, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning framework, Craft (Chaos Random Forest), offers rapid and accurate dengue virus subtyping. This tool significantly improves upon existing methods for tracking viral evolution and disease surveillance.

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Area of Science:

  • Virology
  • Computational Biology
  • Machine Learning

Background:

  • Dengue virus causes nearly 390 million infections yearly, necessitating effective tracking of its evolution.
  • A hierarchical nomenclature system enhances spatial resolution for dengue virus subtyping.
  • Current subtyping tools are computationally intensive, limiting rapid classification.

Purpose of the Study:

  • To introduce Craft (Chaos Random Forest), a machine learning framework for dengue virus subtyping.
  • To evaluate Craft's speed and accuracy compared to existing dengue subtyping tools.

Main Methods:

  • Development of a novel machine learning framework named Craft (Chaos Random Forest).
  • Benchmarking Craft against established tools like Genome Detective, GLUE, and NextClade.
  • Testing Craft's accuracy on a hold-out dataset and its performance with short sequence segments.

Main Results:

  • Craft achieves 99.5% accuracy on a hold-out test set.
  • Craft processes over 140,000 sequences per minute, demonstrating superior speed.
  • High accuracy is maintained even with sequence segments as short as 700 nucleotides.

Conclusions:

  • Craft provides a faster and highly accurate alternative for dengue virus subtyping.
  • The framework aids in efficient viral evolution tracking and public health surveillance.
  • Machine learning offers a promising approach for rapid genomic epidemiology.