Neoantigen Detection Using Transformers and Transfer Learning in the Cancer Immunology Context

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Resumen

Neoantigen detection is the most critical step in developing personalized vaccines in cancer immunology. However, neoantigen detection depends on the correct pMHC binding and presentation prediction. Furthermore, transformers and transfer learning have a high impact on NLP tasks. Since amino acids and proteins are like words and sentences, the pMHC binding and presentation prediction problem could be considered an NLP task. Thus, this work proposed using a BERT architecture pre-trained in 250 million proteins (ESM-1b), then we will use a BiLSTM in cascade. Our preliminary results evaluated a small BERT (TAPE) model achieving 0.80 of AUC on the netMHCpanII3.2 dataset.

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
Páginas97-102
Número de páginas6
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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