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http://hdl.handle.net/2445/178832
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DC Field | Value | Language |
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dc.contributor.author | Wert Carvajal, Carlos | - |
dc.contributor.author | Sánchez García, Rubén | - |
dc.contributor.author | Macías, José R | - |
dc.contributor.author | Sanz Pamplona, Rebeca | - |
dc.contributor.author | Méndez Pérez, Almudena | - |
dc.contributor.author | Alemany Bonastre, Ramon | - |
dc.contributor.author | Veiga, Esteban | - |
dc.contributor.author | Sorzano, Carlos Óscar S. | - |
dc.contributor.author | Muñoz Barrutia, Arrate | - |
dc.date.accessioned | 2021-07-05T09:53:47Z | - |
dc.date.available | 2021-07-05T09:53:47Z | - |
dc.date.issued | 2021-05-24 | - |
dc.identifier.uri | http://hdl.handle.net/2445/178832 | - |
dc.description.abstract | Lack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope binding affinity predictions in mice, and a simplified variant selection process can reduce operational requirements. We have developed a web server tool (NAP-CNB) for a full and automatic pipeline based on recurrent neural networks, to predict putative neoantigens from tumoral RNA sequencing reads. The developed software can estimate H-2 peptide ligands, with an AUC comparable or superior to state-of-the-art methods, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the system's predictive capabilities, and we report its putative neoantigens. NAP-CNB web server is freely available at http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section. | - |
dc.format.extent | 10 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Springer Science and Business Media LLC | - |
dc.relation.isformatof | Reproducció del document publicat a: https://doi.org/10.1038/s41598-021-89927-5 | - |
dc.relation.ispartof | Scientific Reports, 2021, vol. 11, num. 10780 | - |
dc.relation.uri | https://doi.org/10.1038/s41598-021-89927-5 | - |
dc.rights | cc by (c) Wert Carvajal, Carlos et al., 2021 | - |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.source | Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL)) | - |
dc.subject.classification | Càncer | - |
dc.subject.classification | Immunoteràpia | - |
dc.subject.classification | Cèl·lules T | - |
dc.subject.other | Cancer | - |
dc.subject.other | Immunotherapy | - |
dc.subject.other | T cells | - |
dc.title | Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool | - |
dc.type | info:eu-repo/semantics/article | - |
dc.date.updated | 2021-07-02T10:33:12Z | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
dc.identifier.pmid | 34031450 | - |
Appears in Collections: | Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL)) |
Files in This Item:
File | Description | Size | Format | |
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s41598-021-89927-5.pdf | 1.22 MB | Adobe PDF | View/Open |
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