Season combinatorial intervention predictions with Salt & Peper
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911854042808320 |
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| author | Gaudelet, Thomas Del Vecchio, Alice Carrami, Eli M Cudini, Juliana Kapourani, Chantriolnt-Andreas Uhler, Caroline Edwards, Lindsay |
| author_facet | Gaudelet, Thomas Del Vecchio, Alice Carrami, Eli M Cudini, Juliana Kapourani, Chantriolnt-Andreas Uhler, Caroline Edwards, Lindsay |
| contents | Interventions play a pivotal role in the study of complex biological systems. In drug discovery, genetic interventions (such as CRISPR base editing) have become central to both identifying potential therapeutic targets and understanding a drug's mechanism of action. With the advancement of CRISPR and the proliferation of genome-scale analyses such as transcriptomics, a new challenge is to navigate the vast combinatorial space of concurrent genetic interventions. Addressing this, our work concentrates on estimating the effects of pairwise genetic combinations on the cellular transcriptome. We introduce two novel contributions: Salt, a biologically-inspired baseline that posits the mostly additive nature of combination effects, and Peper, a deep learning model that extends Salt's additive assumption to achieve unprecedented accuracy. Our comprehensive comparison against existing state-of-the-art methods, grounded in diverse metrics, and our out-of-distribution analysis highlight the limitations of current models in realistic settings. This analysis underscores the necessity for improved modelling techniques and data acquisition strategies, paving the way for more effective exploration of genetic intervention effects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16907 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Season combinatorial intervention predictions with Salt & Peper Gaudelet, Thomas Del Vecchio, Alice Carrami, Eli M Cudini, Juliana Kapourani, Chantriolnt-Andreas Uhler, Caroline Edwards, Lindsay Genomics Machine Learning Cell Behavior Interventions play a pivotal role in the study of complex biological systems. In drug discovery, genetic interventions (such as CRISPR base editing) have become central to both identifying potential therapeutic targets and understanding a drug's mechanism of action. With the advancement of CRISPR and the proliferation of genome-scale analyses such as transcriptomics, a new challenge is to navigate the vast combinatorial space of concurrent genetic interventions. Addressing this, our work concentrates on estimating the effects of pairwise genetic combinations on the cellular transcriptome. We introduce two novel contributions: Salt, a biologically-inspired baseline that posits the mostly additive nature of combination effects, and Peper, a deep learning model that extends Salt's additive assumption to achieve unprecedented accuracy. Our comprehensive comparison against existing state-of-the-art methods, grounded in diverse metrics, and our out-of-distribution analysis highlight the limitations of current models in realistic settings. This analysis underscores the necessity for improved modelling techniques and data acquisition strategies, paving the way for more effective exploration of genetic intervention effects. |
| title | Season combinatorial intervention predictions with Salt & Peper |
| topic | Genomics Machine Learning Cell Behavior |
| url | https://arxiv.org/abs/2404.16907 |