A1 Refereed original research article in a scientific journal
Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks
Authors: Saldivar-Espinoza Bryan, Macip Guillem, Garcia-Segura Pol, Mestres-Truyol Júlia, Puigbò Pere, Cereto-Massague Adrià, Pujadas Gerard, Garcia-Vallve Santiago
Publisher: MDPI
Publication year: 2022
Journal: International Journal of Molecular Sciences
Journal name in source: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Journal acronym: INT J MOL SCI
Article number: 14683
Volume: 23
Issue: 23
Number of pages: 17
eISSN: 1422-0067
DOI: https://doi.org/10.3390/ijms232314683
Web address : https://www.mdpi.com/1422-0067/23/23/14683
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/178071596
Predicting SARS-CoV-2 mutations is difficult, but predicting recurrent mutations driven by the host, such as those caused by host deaminases, is feasible. We used machine learning to predict which positions from the SARS-CoV-2 genome will hold a recurrent mutation and which mutations will be the most recurrent. We used data from April 2021 that we separated into three sets: a training set, a validation set, and an independent test set. For the test set, we obtained a specificity value of 0.69, a sensitivity value of 0.79, and an Area Under the Curve (AUC) of 0.8, showing that the prediction of recurrent SARS-CoV-2 mutations is feasible. Subsequently, we compared our predictions with updated data from January 2022, showing that some of the false positives in our prediction model become true positives later on. The most important variables detected by the model's Shapley Additive exPlanation (SHAP) are the nucleotide that mutates and RNA reactivity. This is consistent with the SARS-CoV-2 mutational bias pattern and the preference of some host deaminases for specific sequences and RNA secondary structures. We extend our investigation by analyzing the mutations from the variants of concern Alpha, Beta, Delta, Gamma, and Omicron. Finally, we analyzed amino acid changes by looking at the predicted recurrent mutations in the M-pro and spike proteins.
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