New methods for drug synergy prediction: a mini-review
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914755011149824 |
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| author | Abbasi, Fatemeh Rousu, Juho |
| author_facet | Abbasi, Fatemeh Rousu, Juho |
| contents | In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of the field is witnessed in the more than thirty original machine learning methods published since 2021, a clear majority of them based on deep learning techniques. We aim to put these papers under a unifying lens by highlighting the core technologies, the data sources, the input data types and synergy scores used in the methods, as well as the prediction scenarios and evaluation protocols that the papers deal with. Our finding is that the best methods accurately solve the synergy prediction scenarios involving known drugs or cell lines while the scenarios involving new drugs or cell lines still fall short of an accurate prediction level. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02484 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | New methods for drug synergy prediction: a mini-review Abbasi, Fatemeh Rousu, Juho Machine Learning Artificial Intelligence Quantitative Methods I.2.6; J.3 In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of the field is witnessed in the more than thirty original machine learning methods published since 2021, a clear majority of them based on deep learning techniques. We aim to put these papers under a unifying lens by highlighting the core technologies, the data sources, the input data types and synergy scores used in the methods, as well as the prediction scenarios and evaluation protocols that the papers deal with. Our finding is that the best methods accurately solve the synergy prediction scenarios involving known drugs or cell lines while the scenarios involving new drugs or cell lines still fall short of an accurate prediction level. |
| title | New methods for drug synergy prediction: a mini-review |
| topic | Machine Learning Artificial Intelligence Quantitative Methods I.2.6; J.3 |
| url | https://arxiv.org/abs/2404.02484 |