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

Machaca V.E.; Goyzueta V.; Cruz M.; Tupac Y. · Lecture Notes in Networks and Systems (2023) · DOI: 10.1007/978-3-031-38079-2_2

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.

Instituciones: Universidad La Salle; Universidad Católica San Pablo