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Accurate TCR-pMHC interaction prediction using a BERT-based transfer learning method.
- Source :
-
Briefings in Bioinformatics . Jan2024, Vol. 25 Issue 1, p1-10. 10p. - Publication Year :
- 2024
-
Abstract
- Accurate prediction of TCR-pMHC binding is important for the development of cancer immunotherapies, especially TCR-based agents. Existing algorithms often experience diminished performance when dealing with unseen epitopes, primarily due to the complexity in TCR-pMHC recognition patterns and the scarcity of available data for training. We have developed a novel deep learning model, 'TCR Antigen Binding Recognition' based on BERT, named as TABR-BERT. Leveraging BERT's potent representation learning capabilities, TABR-BERT effectively captures essential information regarding TCR-pMHC interactions from TCR sequences, antigen epitope sequences and epitope-MHC binding. By transferring this knowledge to predict TCR-pMHC recognition, TABR-BERT demonstrated better results in benchmark tests than existing methods, particularly for unseen epitopes. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14675463
- Volume :
- 25
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Briefings in Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 174953974
- Full Text :
- https://doi.org/10.1093/bib/bbad436