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GEARS, a machine learning model informed by biological knowledge of gene–gene relationships, effectively predicts transcriptional responses to multi-gene perturbations. GEARS can predict the effects of perturbing previously unperturbed genes and detects non-additive interactions, such as synergy, when predicting combinatorial perturbation outcomes. Thus, GEARS expands insights gained from perturbational screens.
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References
Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016). This paper describes the assay used to measure single-cell transcriptional responses to perturbation.
Norman, T. M. et al. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science 365, 786–793 (2019). These authors studied genetic interactions using a multi-gene perturbation screen.
Kamimoto, K. et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023). This article presents an alternative in silico gene perturbation model.
Replogle, J. M. et al. Mapping information-rich genotype-phenotype landscapes with genome-scale perturb-seq. Cell 185, 2559–2575 (2022). This article presents a genome-wide perturbation screen.
Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015). This article presents the importance of genetic information for drug efficacy.
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This is a summary of: Roohani, Y., Huang, K. & Leskovec, J. Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01905-6 (2023)
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Machine learning predicts cellular response to genetic perturbation.
Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01907-4
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DOI: https://doi.org/10.1038/s41587-023-01907-4
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