Application of RotaSVM for HLA class II Protein-Peptide Interaction Prediction
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , , , |
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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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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
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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1799136581918916608 |