Deep Learning and Transformers in MHC-Peptide Binding and Presentation Towards Personalized Vaccines in Cancer Immunology: A Brief Review

Vicente Enrique Machaca, Valeria Goyzueta, Maria Cruz, Yvan Tupac

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Cancer immunology is a new alternative to traditional cancer treatments like radiotherapy and chemotherapy. There are some strategies, but neoantigen detection for developing cancer vaccines are methods with a high impact in recent years. However, neoantigen detection depends on the correct prediction of peptide-MHC binding. Furthermore, transformers are considered a revolution in artificial intelligence with a high impact on NLP tasks. Since amino acids and proteins could be considered like words and sentences, the peptide-MHC binding prediction problem could be seen as a NLP task. Therefore, in this work, we performed a systematic literature review of deep learning and transformer methods used in peptide-MHC binding and presentation prediction. We analyzed how ANNs, CNNs, RNNs, and Transformer are used.

Idioma originalInglés
Título de la publicación alojadaPractical Applications of Computational Biology and Bioinformatics, 17th International Conference (PACBB 2023)
EditoresMiguel Rocha, Florentino Fdez-Riverola, Mohd Saberi Mohamad, Ana Belén Gil-González
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas14-23
Número de páginas10
ISBN (versión impresa)9783031380785
DOI
EstadoPublicada - 2023
Publicado de forma externa
Evento17th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2023 - Guimaraes, Portugal
Duración: 12 jul. 202314 jul. 2023

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen743 LNNS
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

Conferencia17th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2023
País/TerritorioPortugal
CiudadGuimaraes
Período12/07/2314/07/23

Nota bibliográfica

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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