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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
1Laboratório de Genética Celular e Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Plos One
|December 12, 2022
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.
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.
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