Deep learning and transformers in MHC-peptide binding and presentation towards personalized vaccines in cancer immunology: a brief review

• Vicente Enrique Machaca ^[2] ; Valeria Goyzueta ^[2] ; Maria Cruz ^[1] ; Yvan Tupac ^[1]
  1. [1] Universidad Católica San Pablo

     Universidad Católica San Pablo

     Arequipa, Perú

     
  2. [2] Universidad La Salle, Arequipa, Peru
• Localización: Practical applications of computational biology and bioinformatics, 17th International Conference (PACBB 2023) / Miguel Rocha (ed. lit.), Florentino Fernández Riverola (ed. lit.), Mohd Saberi Mohamad (ed. lit.), Ana Belén Gil González (ed. lit.), 2023, ISBN 978-3-031-38078-5, págs. 14-23
• Idioma: inglés
• Enlaces
  • Texto Completo Libro
• 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.