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dc.contributor.authorGutiérrez Cárdenas, Juan Manuel
dc.contributor.authorWang, Zenghui
dc.contributor.otherGutiérrez Cárdenas, Juan Manuel
dc.date.accessioned2023-02-08T20:43:28Z
dc.date.available2023-02-08T20:43:28Z
dc.date.issued2022
dc.identifier.citationGutiérrez-Cárdenas J. & Wang, Z. (2022). Prediction of binding miRNAs involved with immune genes to the SARS-CoV-2 by using sequence features extraction and One-class SVM. In Informatics in Medicine Unlocked, (30), 1-8. https://doi.org/10.1016/j.imu.2022.100958es_PE
dc.identifier.issn2352-9148
dc.identifier.urihttps://hdl.handle.net/20.500.12724/17576
dc.description.abstractThe prediction of host human miRNA binding to the SARS-COV-2-CoV-2 RNA sequence is of particular interest. This biological process could lead to virus repression, serve as biomarkers for diagnosis, or as potential treatments for this disease. One source of concern is attempting to uncover the viral regions in which this binding could occur, as well as how these miRNAs binding could affect the SARS-COV-2 virus's processes. Using extracted sequence features from this base pairing, we predicted the relationships between miRNAs that interact with genes involved in immune function and bind to the SARS-COV-2 genome in their 5' UTR region. We compared two supervised models, SVM and Random Forest, with an unsupervised One-Class SVM. When the results of the confusion matrices were inspected, the results of the supervised models were misleading, resulting in a Type II error. However, with the latter model, we achieved an average accuracy of 92%, sensitivity of 96.18%, and specificity of 78%. We hypothesize that studying the bind of miRNAs that affect immunological genes and bind to the SARS-COV-2 virus will lead to potential genetic therapies for fighting the disease or understanding how the immune system is affected when this type of viral infection occurs.en_EN
dc.formatapplication/html
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofurn:issn:23529148
dc.rightsinfo:eu-repo/semantics/openAccess*
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.sourceRepositorio Institucional Ulima
dc.sourceUniversidad de Lima
dc.subjectPandemicsen_EN
dc.subjectRNA virusesen_EN
dc.subjectCOVID-19en_EN
dc.subjectPandemiases_PE
dc.subjectVirus ARNes_PE
dc.subjectSARS-CoV-2 (Virus)es_PE
dc.subject.classificationPendientees_PE
dc.titlePrediction of binding miRNAs involved with immune genes to the SARS-CoV-2 by using sequence features extraction and One-class SVMen_EN
dc.typeinfo:eu-repo/semantics/article
dc.type.otherArtículo en Scopus
ulima.areas.lineasdeinvestigacionCalidad de vida y bienestar / Saludes_PE
dc.identifier.journalInformatics in Medicine Unlocked
dc.publisher.countryGB
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#3.00.00
dc.identifier.doihttps://doi.org/10.1016/j.imu.2022.100958
ulima.cat9
ulima.autor.afiliacionUniversidad de Lima
ulima.autor.carreraIngeniería de Sistemas
dc.identifier.isni0000000121541816
dc.identifier.scopusid2-s2.0-85129982354


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