Machine learning predicts cellular response to genetic perturbation

    Nature Biotechnology

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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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    Fig. 1: GEARS performs in silico gene perturbation.

    References

    1. 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.

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    2. 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.

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    3. 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.

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    4. 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.

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    5. 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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    Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

    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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