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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.19349 |
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| _version_ | 1866911970895069184 |
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| author | Tu, Tengyao Zeng, Wei Zhao, Kun Zhang, Zhenyu |
| author_facet | Tu, Tengyao Zeng, Wei Zhao, Kun Zhang, Zhenyu |
| contents | Researching the specificity of TCR contributes to the development of immunotherapy and provides new opportunities and strategies for personalized cancer immunotherapy. Therefore, we established a TCR generative specificity detection framework consisting of an antigen selector and a TCR classifier based on the Random Forest algorithm, aiming to efficiently screen out TCRs and target antigens and achieve TCR specificity prediction. Furthermore, we used the k-fold validation method to compare the performance of our model with ordinary deep learning methods. The result proves that adding a classifier to the model based on the random forest algorithm is very effective, and our model generally outperforms ordinary deep learning methods. Moreover, we put forward feasible optimization suggestions for the shortcomings and challenges of our model found during model implementation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19349 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting T-Cell Receptor Specificity Tu, Tengyao Zeng, Wei Zhao, Kun Zhang, Zhenyu Quantitative Methods Artificial Intelligence Researching the specificity of TCR contributes to the development of immunotherapy and provides new opportunities and strategies for personalized cancer immunotherapy. Therefore, we established a TCR generative specificity detection framework consisting of an antigen selector and a TCR classifier based on the Random Forest algorithm, aiming to efficiently screen out TCRs and target antigens and achieve TCR specificity prediction. Furthermore, we used the k-fold validation method to compare the performance of our model with ordinary deep learning methods. The result proves that adding a classifier to the model based on the random forest algorithm is very effective, and our model generally outperforms ordinary deep learning methods. Moreover, we put forward feasible optimization suggestions for the shortcomings and challenges of our model found during model implementation. |
| title | Predicting T-Cell Receptor Specificity |
| topic | Quantitative Methods Artificial Intelligence |
| url | https://arxiv.org/abs/2407.19349 |