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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.10016 |
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| _version_ | 1866914648713854976 |
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| author | Chowdhury, Shaika Rajaganapathy, Sivaraman Sun, Lichao Cerhan, James Zong, Nansu |
| author_facet | Chowdhury, Shaika Rajaganapathy, Sivaraman Sun, Lichao Cerhan, James Zong, Nansu |
| contents | In this study, we investigated the potential of GPT-3 for the anti-cancer drug sensitivity prediction task using structured pharmacogenomics data across five tissue types and evaluated its performance with zero-shot prompting and fine-tuning paradigms. The drug's smile representation and cell line's genomic mutation features were predictive of the drug response. The results from this study have the potential to pave the way for designing more efficient treatment protocols in precision oncology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_10016 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Evaluation of GPT-3 for Anti-Cancer Drug Sensitivity Prediction Chowdhury, Shaika Rajaganapathy, Sivaraman Sun, Lichao Cerhan, James Zong, Nansu Machine Learning Artificial Intelligence In this study, we investigated the potential of GPT-3 for the anti-cancer drug sensitivity prediction task using structured pharmacogenomics data across five tissue types and evaluated its performance with zero-shot prompting and fine-tuning paradigms. The drug's smile representation and cell line's genomic mutation features were predictive of the drug response. The results from this study have the potential to pave the way for designing more efficient treatment protocols in precision oncology. |
| title | Evaluation of GPT-3 for Anti-Cancer Drug Sensitivity Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2309.10016 |