Abstract
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.
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Machaca, V.E., Goyzueta, V., Cruz, M., Tupac, Y. (2023). Deep Learning and Transformers in MHC-Peptide Binding and Presentation Towards Personalized Vaccines in Cancer Immunology: A Brief Review. In: Rocha, M., Fdez-Riverola, F., Mohamad, M.S., Gil-González, A.B. (eds) Practical Applications of Computational Biology and Bioinformatics, 17th International Conference (PACBB 2023). PACBB 2023. Lecture Notes in Networks and Systems, vol 743. Springer, Cham. https://doi.org/10.1007/978-3-031-38079-2_2
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