Deep unsupervised learning methods for the identification and characterization of TCR specificity to Sars-Cov-2

Miraoui, Y. N.

doi:10.1101/2023.09.05.556326

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bioRxiv · 10.1101/2023.09.05.556326

Deep unsupervised learning methods for the identification and characterization of TCR specificity to Sars-Cov-2

Miraoui, Y. N.

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Abstract

The 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.

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Miraoui, Y. N.. 2023-09-05. Deep unsupervised learning methods for the identification and characterization of TCR specificity to Sars-Cov-2. https://doi.org/10.1101/2023.09.05.556326

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