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Machine learning methods for predicting human-adaptive influenza A virus reassortment based on intersegment

Dan-Dan Zeng1,2, Yu-Rong Cai2, Sen Zhang2

  • 1College of Veterinary Medicine, Shanxi Agricultural University, Jinzhong, China.

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|April 7, 2025
PubMed
Summary

This study reveals distinct genomic nucleotide composition in human-adapted Influenza A viruses (IAVs), enabling prediction of viral reassortment through machine learning. Understanding these genetic features is key to predicting IAV evolution and adaptation.

Keywords:
H1N1influenza A viruses (IAVs)machine learningnucleotide compositionreassortment

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Area of Science:

  • Virology
  • Genomics
  • Bioinformatics

Background:

  • The mechanisms driving Influenza A virus (IAV) inter-segment reassortment remain unclear.
  • Understanding viral genetic compatibility is crucial for predicting IAV adaptation and evolution.

Purpose of the Study:

  • To analyze viral nucleotide composition (NC) and inter-segment NC correlations in IAVs.
  • To develop machine learning (ML) models for predicting IAV reassortment and human adaptation based on NC features.

Main Methods:

  • Unsupervised ML methods were employed to differentiate NC between human-adapted and zoonotic IAVs.
  • Supervised ML models, including random forest classifier (RFC) and multilayer perceptron (MLP), were developed to predict human adaptation.
  • Analysis included nucleotide frequencies (T, C, A, G) and GC/AT content across viral segments.

Main Results:

  • Distinct NC patterns were observed in specific IAV segments (PB2, PB1, PA, NP, M1, NS1) between mammalian and avian IAVs.
  • Inter-segment NC correlations were identified, with RNPplus NC negatively correlating with EPplus (HA, NA, M1) NC.
  • Human-adapted NC profiles accurately distinguished human IAVs from avian IAVs, with simulated IAVs showing high adaptation potential.

Conclusions:

  • A distinct, human adaptation-specific genomic NC exists between human and avian IAVs.
  • Inter-segment NC correlations impose constraints on viral segment reassortment.
  • This study introduces a novel ML-based strategy for predicting IAV reassortment using viral genetic compatibility.