Application of RotaSVM for HLA class II Protein-Peptide Interaction Prediction

Detalhes bibliográficos
Autor(a) principal: Bhowmick, ShibSankar
Data de Publicação: 2014
Outros Autores: Saha, Indrajit, Mazzocco, Giovanni, Maulik, Ujjwal, Rato, Luis, Bhattacharjee, Debotosh, Plewczynski, Dariusz
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/18003
https://doi.org/https://dx.doi.org/10.5220/0004804801780185
Resumo: In this article, the recently developed RotaSVM is used for accurate prediction of binding peptides to Human Leukocyte Antigens class II (HLA class II) proteins. The HLA II - peptide complexes are generated in the antigen presenting cells (APC) and transported to the cell membrane to elicit an immune response via T-cell activation. The understanding of HLA class II protein-peptide binding interaction facilitates the design of peptide-based vaccine, where the high rate of polymorphisms in HLA class II molecules poses a big challenge. To determine the binding activity of 636 non-redundant peptides, a set of 27 HLA class II proteins are considered in the present study. The prediction of HLA class II - peptide binding is carried out by an ensemble classifier called RotaSVM. In RotaSVM, the feature selection scheme generates bootstrap samples that are further used to create a diverse set of features using Principal Component Analysis. Thereafter, Support Vector Machines are trained with th ese bootstrap samples with the integration of their original feature values. The effectiveness of the RotaSVM for HLA class II protein-peptide binding prediction is demonstrated in comparison with other traditional classifiers by evaluating several validity measures with the visual plot of ROC curves. Finally, Friedman test is conducted to judge the statistical significance of RotaSVM in prediction of peptides binding to HLA class II proteins.
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spelling Application of RotaSVM for HLA class II Protein-Peptide Interaction PredictionHLA Class IIMachine LearningMHCPeptide BindingT Cell EpitopesIn this article, the recently developed RotaSVM is used for accurate prediction of binding peptides to Human Leukocyte Antigens class II (HLA class II) proteins. The HLA II - peptide complexes are generated in the antigen presenting cells (APC) and transported to the cell membrane to elicit an immune response via T-cell activation. The understanding of HLA class II protein-peptide binding interaction facilitates the design of peptide-based vaccine, where the high rate of polymorphisms in HLA class II molecules poses a big challenge. To determine the binding activity of 636 non-redundant peptides, a set of 27 HLA class II proteins are considered in the present study. The prediction of HLA class II - peptide binding is carried out by an ensemble classifier called RotaSVM. In RotaSVM, the feature selection scheme generates bootstrap samples that are further used to create a diverse set of features using Principal Component Analysis. Thereafter, Support Vector Machines are trained with th ese bootstrap samples with the integration of their original feature values. The effectiveness of the RotaSVM for HLA class II protein-peptide binding prediction is demonstrated in comparison with other traditional classifiers by evaluating several validity measures with the visual plot of ROC curves. Finally, Friedman test is conducted to judge the statistical significance of RotaSVM in prediction of peptides binding to HLA class II proteins.Science and Technology Publications2016-03-14T12:04:15Z2016-03-142014-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/18003http://hdl.handle.net/10174/18003https://doi.org/https://dx.doi.org/10.5220/0004804801780185porBhowmick S., Saha I., Mazzocco G., Maulik U., Rato L., Bhattacharjee D. and Plewczynski D. (2014). Application of RotaSVM for HLA Class II Protein-Peptide Interaction Prediction. In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2014), ISBN 978-989-758-012-3, pages 178-185. DOI: 10.5220/0004804801780185ndndndlmr@uevora.ptndndnd498Bhowmick, ShibSankarSaha, IndrajitMazzocco, GiovanniMaulik, UjjwalRato, LuisBhattacharjee, DebotoshPlewczynski, Dariuszinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:05:50Zoai:dspace.uevora.pt:10174/18003Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:09:58.614571Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Application of RotaSVM for HLA class II Protein-Peptide Interaction Prediction
title Application of RotaSVM for HLA class II Protein-Peptide Interaction Prediction
spellingShingle Application of RotaSVM for HLA class II Protein-Peptide Interaction Prediction
Bhowmick, ShibSankar
HLA Class II
Machine Learning
MHC
Peptide Binding
T Cell Epitopes
title_short Application of RotaSVM for HLA class II Protein-Peptide Interaction Prediction
title_full Application of RotaSVM for HLA class II Protein-Peptide Interaction Prediction
title_fullStr Application of RotaSVM for HLA class II Protein-Peptide Interaction Prediction
title_full_unstemmed Application of RotaSVM for HLA class II Protein-Peptide Interaction Prediction
title_sort Application of RotaSVM for HLA class II Protein-Peptide Interaction Prediction
author Bhowmick, ShibSankar
author_facet Bhowmick, ShibSankar
Saha, Indrajit
Mazzocco, Giovanni
Maulik, Ujjwal
Rato, Luis
Bhattacharjee, Debotosh
Plewczynski, Dariusz
author_role author
author2 Saha, Indrajit
Mazzocco, Giovanni
Maulik, Ujjwal
Rato, Luis
Bhattacharjee, Debotosh
Plewczynski, Dariusz
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Bhowmick, ShibSankar
Saha, Indrajit
Mazzocco, Giovanni
Maulik, Ujjwal
Rato, Luis
Bhattacharjee, Debotosh
Plewczynski, Dariusz
dc.subject.por.fl_str_mv HLA Class II
Machine Learning
MHC
Peptide Binding
T Cell Epitopes
topic HLA Class II
Machine Learning
MHC
Peptide Binding
T Cell Epitopes
description In this article, the recently developed RotaSVM is used for accurate prediction of binding peptides to Human Leukocyte Antigens class II (HLA class II) proteins. The HLA II - peptide complexes are generated in the antigen presenting cells (APC) and transported to the cell membrane to elicit an immune response via T-cell activation. The understanding of HLA class II protein-peptide binding interaction facilitates the design of peptide-based vaccine, where the high rate of polymorphisms in HLA class II molecules poses a big challenge. To determine the binding activity of 636 non-redundant peptides, a set of 27 HLA class II proteins are considered in the present study. The prediction of HLA class II - peptide binding is carried out by an ensemble classifier called RotaSVM. In RotaSVM, the feature selection scheme generates bootstrap samples that are further used to create a diverse set of features using Principal Component Analysis. Thereafter, Support Vector Machines are trained with th ese bootstrap samples with the integration of their original feature values. The effectiveness of the RotaSVM for HLA class II protein-peptide binding prediction is demonstrated in comparison with other traditional classifiers by evaluating several validity measures with the visual plot of ROC curves. Finally, Friedman test is conducted to judge the statistical significance of RotaSVM in prediction of peptides binding to HLA class II proteins.
publishDate 2014
dc.date.none.fl_str_mv 2014-03-01T00:00:00Z
2016-03-14T12:04:15Z
2016-03-14
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10174/18003
http://hdl.handle.net/10174/18003
https://doi.org/https://dx.doi.org/10.5220/0004804801780185
url http://hdl.handle.net/10174/18003
https://doi.org/https://dx.doi.org/10.5220/0004804801780185
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv Bhowmick S., Saha I., Mazzocco G., Maulik U., Rato L., Bhattacharjee D. and Plewczynski D. (2014). Application of RotaSVM for HLA Class II Protein-Peptide Interaction Prediction. In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2014), ISBN 978-989-758-012-3, pages 178-185. DOI: 10.5220/0004804801780185
nd
nd
nd
lmr@uevora.pt
nd
nd
nd
498
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Science and Technology Publications
publisher.none.fl_str_mv Science and Technology Publications
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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