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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8255| Deep unsupervised learning methods for the identification and characterization of TCR specificity to Sars-Cov-2 | |
| Yanis Miraoui | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2023.09.05.556326 | |
| https://www.biorxiv.org/content/10.1101/2023.09.05.556326v1 | |
| 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. | |
| bioRxiv | |
| 05-09-2023 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| Deep unsupervised learning methods for the identification and characterization of TCR specificity to Sars-Cov-2.pdf | 19.22 MB | Adobe PDF | Visualizar/Abrir |