A novel ensemble fuzzy classification model in SARS-CoV-2 B-cell epitope identification for development of protein-based vaccine

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dc.authorscopusid55808009200
dc.authorscopusid56539994200
dc.contributor.authorÖzger, Zeynep Banu
dc.contributor.authorCihan, Pınar
dc.date.accessioned2022-05-11T14:15:58Z
dc.date.available2022-05-11T14:15:58Z
dc.date.issued2022
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractB-cell epitope prediction research has received growing interest since the development of the first method. B-cell epitope identification with the aid of an accurate prediction method is one of the most important steps in epitope-based vaccine development, immunodiagnostic testing, antibody production, disease diagnosis, and treatment. Nevertheless, using experimental methods in epitope mapping is very time-consuming, costly, and labor-intensive. Therefore, although successful predictions with in silico methods are very important in epitope prediction, there are limited studies in this area. The aim of this study is to propose a new approach for successfully predicting B-cell epitopes for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, the SARS-CoV B-cell epitope prediction performances of different fuzzy learning classification models genetic cooperative competitive learning (GCCL), fuzzy genetics-based machine learning (GBML), Chi's method (CHI), Ishibuchi's method with weight factor (W), structural learning algorithm on vague environment (SLAVE) and the state-of-the-art ensemble fuzzy classification model were compared. The obtained results showed that the proposed ensemble approach has the lowest error in SARS-CoV B-cell epitope estimation compared to the base fuzzy learners (average error rates; ensemble fuzzy=8.33, GCCL=30.42, GBML=23.82, CHI=29.17, W=46.25, and SLAVE=20.42). SARS-CoV and SARS-CoV-2 have high genome similarities. Therefore, the most successful method determined for SARS-CoV B-cell epitope prediction was used in SARS-CoV-2 cell epitope prediction. Finally, the eventual B-cell epitope prediction results obtained for SARS-CoV-2 with the ensemble fuzzy classification model were compared with the epitope sequences predicted by the BepiPred server and immunoinformatics studies in the literature for the same protein sequences according to VaxiJen 2.0 scores. We hope that the developed epitope prediction method will help design effective vaccines and drugs against future outbreaks of the coronavirus family, especially SARS-CoV-2 and its possible mutations. © 2021 Elsevier B.V.
dc.description.sponsorship121E326
dc.description.sponsorshipThis study was supported by The Scientific and Technological Research Council of Turkey-TÜBİTAK (Project Number: 121E326 ).
dc.description.sponsorshipThis study was supported by The Scientific and Technological Research Council of Turkey-T?B?TAK (Project Number: 121E326).
dc.identifier.doi10.1016/j.asoc.2021.108280
dc.identifier.issn1568-4946
dc.identifier.scopus2-s2.0-85121288563
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2021.108280
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6136
dc.identifier.volume116
dc.indekslendigikaynakScopus
dc.institutionauthorCihan, Pınar
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofApplied Soft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectB-cell
dc.subjectEpitope
dc.subjectFuzzy learning
dc.subjectSARS-CoV
dc.subjectSARS-CoV-2
dc.subjectSpike protein
dc.subjectCells
dc.subjectCytology
dc.subjectDiagnosis
dc.subjectDiseases
dc.subjectEpitopes
dc.subjectForecasting
dc.subjectFuzzy systems
dc.subjectLearning algorithms
dc.subjectMachine learning
dc.subjectProteins
dc.subjectVaccines
dc.subjectB cell epitopes predictions
dc.subjectB cells
dc.subjectCell epitopes
dc.subjectEpitope predictions
dc.subjectFuzzy learning
dc.subjectPrediction methods
dc.subjectSARS-CoV
dc.subjectSevere acute respiratory syndrome coronavirus
dc.subjectSevere acute respiratory syndrome coronavirus 2
dc.subjectSpike protein
dc.subjectSARS
dc.titleA novel ensemble fuzzy classification model in SARS-CoV-2 B-cell epitope identification for development of protein-based vaccine
dc.typeArticle

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