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| Natura: | Recurso digital |
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Zenodo
2026
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| Accesso online: | https://doi.org/10.5281/zenodo.17364504 |
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| _version_ | 1866902194313232384 |
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| author | Li, Chia-Wei |
| author_facet | Li, Chia-Wei |
| contents | <p>This record provides the single-cell immune repertoire sequencing (scRepertoire-seq) dataset and the corresponding optimization of the STM004 single-chain variable fragment (scFv) using a deep reinforcement learning model, MatuRL, as described in “AI-guided scFv engineering enhances T cell–specific cytotoxicity toward cancer cells.”Both datasets support the analyses and findings presented in the study.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17364504 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Datasets for AI-guided scFv engineering and single-cell immune repertoire profiling Li, Chia-Wei <p>This record provides the single-cell immune repertoire sequencing (scRepertoire-seq) dataset and the corresponding optimization of the STM004 single-chain variable fragment (scFv) using a deep reinforcement learning model, MatuRL, as described in “AI-guided scFv engineering enhances T cell–specific cytotoxicity toward cancer cells.”Both datasets support the analyses and findings presented in the study.</p> |
| title | Datasets for AI-guided scFv engineering and single-cell immune repertoire profiling |
| url | https://doi.org/10.5281/zenodo.17364504 |