Autor según el artículo: Saldivar-Espinoza, Bryan; Macip, Guillem; Garcia-Segura, Pol; Mestres-Truyol, Julia; Puigbo, Pere; Cereto-Massague, Adria; Pujadas, Gerard; Garcia-Vallve, Santiago;
Departamento: Bioquímica i Biotecnologia
Autor/es de la URV: Cereto Massagué, Adrián José / Garcia Vallve, Santiago / Macip Sancho, Guillem / PUIGBÒ AVALOS, PEDRO / Pujadas Anguiano, Gerard / Saldivar Espinoza, Bryan Percy
Palabras clave: Sars-cov-2 Mutations Machine learning Covid-19
Resumen: 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.
Áreas temáticas: Zootecnia / recursos pesqueiros Spectroscopy Saúde coletiva Química Psicología Physical and theoretical chemistry Organic chemistry Odontología Nutrição Molecular biology Medicine (miscellaneous) Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Interdisciplinar Inorganic chemistry Geociências Farmacia Engenharias iv Engenharias ii Engenharias i Educação física Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Chemistry, multidisciplinary Catalysis Biotecnología Biodiversidade Biochemistry & molecular biology Astronomia / física
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: adrianjose.cereto@urv.cat gerard.pujadas@urv.cat santi.garcia-vallve@urv.cat guillem.macip@estudiants.urv.cat guillem.macip@estudiants.urv.cat bryanpercy.saldivar@estudiants.urv.cat bryanpercy.saldivar@estudiants.urv.cat
Identificador del autor: 0000-0003-2598-8089 0000-0002-0348-7497 0000-0002-9667-2818 0000-0002-9667-2818
Fecha de alta del registro: 2024-03-03
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.mdpi.com/1422-0067/23/23/14683
URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: International Journal Of Molecular Sciences. 23 (23):
Referencia de l'ítem segons les normes APA: Saldivar-Espinoza, Bryan; Macip, Guillem; Garcia-Segura, Pol; Mestres-Truyol, Julia; Puigbo, Pere; Cereto-Massague, Adria; Pujadas, Gerard; Garcia-Val (2022). Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks. International Journal Of Molecular Sciences, 23(23), -. DOI: 10.3390/ijms232314683
DOI del artículo: 10.3390/ijms232314683
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2022
Tipo de publicación: Journal Publications
Repositori URV