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

Arceda V.E.M. · Lecture Notes in Networks and Systems (2023) · DOI: 10.1007/978-3-031-38079-2_10

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.

Instituciones: Universidad La Salle