Abstract
AbstractThe T-cell receptor (TCR) is one of the key players in the immune response to the Sars-Cov-2 virus. In this study, we used deep unsu-pervised learning methods to identify and characterize TCR speci-ficity. Our research focused on developing and applying state-of-the-art modelling techniques, including AutoEncoders, Variational Au-to Encoders and transfer learning with Transformers, to analyze TCR data. Through our experiments and analyses, we have achieved promis-ing results in identifying TCR patterns and understanding TCR speci-ficity for Sars-Cov-2. The insights gained from our research provide valuable tools and knowledge for interpreting the immunological re-sponse to the virus, ultimately contributing to the development of effective vaccines and treatments against the viral infection.
Publisher
Cold Spring Harbor Laboratory
Reference18 articles.
1. Quantitative immunology for physicists;Physics Reports,2020
2. Probing t-cell response by sequence-based probabilistic modeling;PLOS Computational Biology,2021
3. Cheung, K . (2022). Methods for the characterization of specificity in immune re-sponse to sars-cov-2.
4. Davidsen, K. , Olson, B. J., III , W. S. D., Feng, J. , Harkins, E. , Bradley, P. , and IV, F. A. M. (2019). Deep generative models for t cell receptor protein sequences. eLife, 8.
5. Gallagher, J. (2023). New superbug-killing antibiotic discovered using ai. BBC News.