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Machine learning models exploring characteristic single-nucleotide signatures in yellow fever virus.

Álvaro Salgado1, Raquel C de Melo-Minardi2, Marta Giovanetti1,3

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Summary

Machine learning identified genetic signatures in yellow fever virus (YFV) linked to human disease severity and non-human primate (NHP) variations. This approach aids rapid genomic data exploration for emerging infectious diseases.

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Area of Science:

  • Virology
  • Genomics
  • Machine Learning

Background:

  • Yellow fever virus (YFV) causes severe mosquito-borne tropical disease.
  • Recent Brazilian YFV outbreaks highlighted risks in unvaccinated urban populations.

Purpose of the Study:

  • To apply machine learning to YFV genomic sequences.
  • To identify genetic signatures associated with human disease severity and NHP PCR cycle threshold (Ct) values.

Main Methods:

  • Developed a machine learning framework using XGBoost, random forest, and logistic regression.
  • Analyzed 56 human and 27 non-human primate (NHP) YFV genomic sequences.
  • Performed comparative protein structural analysis on identified single nucleotide variations (SNVs).

Main Results:

  • Identified four non-synonymous SNVs in human YFV sequences (NS3, NS4a, NS5 proteins).
  • Detected six non-synonymous SNVs in NHP YFV sequences (E, NS1, NS3, NS5 proteins).
  • Described potential impacts of these SNVs on protein function.

Conclusions:

  • Machine learning offers a versatile and rapid method for initial genomic data exploration.
  • Identified SNVs provide a basis for further investigation into YFV virulence and transmission.