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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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