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Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks.

Bryan Saldivar-Espinoza1, Guillem Macip1, Pol Garcia-Segura1

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Summary
This summary is machine-generated.

Machine learning can predict recurrent SARS-CoV-2 mutations driven by host factors. The model identified key mutation predictors like nucleotide changes and RNA reactivity, showing promise for understanding viral evolution.

Keywords:
COVID-19SARS-CoV-2machine learningmutations

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Area of Science:

  • Virology
  • Computational Biology
  • Genomics

Background:

  • Predicting mutations in SARS-CoV-2 is challenging.
  • Host-driven mutations, particularly those from deaminases, are more predictable.
  • Understanding recurrent mutation patterns is crucial for tracking viral evolution.

Purpose of the Study:

  • To develop a machine learning model for predicting recurrent SARS-CoV-2 mutations.
  • To identify specific genomic positions and mutation types likely to recur.
  • To analyze the impact of host factors on viral mutation patterns.

Main Methods:

  • Utilized machine learning on SARS-CoV-2 genomic data from April 2021.
  • Separated data into training, validation, and independent test sets.
  • Employed Shapley Additive exPlanation (SHAP) to identify key predictive variables.

Main Results:

  • Achieved a specificity of 0.69, sensitivity of 0.79, and AUC of 0.8 in predicting recurrent mutations.
  • Model predictions showed alignment with later data (January 2022), with some false positives becoming true positives.
  • Nucleotide mutation type and RNA reactivity were identified as the most significant predictive factors.

Conclusions:

  • Machine learning offers a feasible approach to predict recurrent SARS-CoV-2 mutations.
  • Host deaminases and RNA characteristics significantly influence SARS-CoV-2 mutation patterns.
  • The study provides insights into mutations within variants of concern and their impact on key proteins (M-pro, spike).