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"key": "adamson2016multiplexed",
"title": "A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response",
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"key": "gonzalez2025combinatorial",
"title": "Combinatorial prediction of therapeutic perturbations using causally inspired neural networks",
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"key": "han2025identifying",
"title": "Identifying an optimal perturbation to induce a desired cell state by generative deep learning",
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"key": "jaitin2016dissecting",
"title": "Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq",
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"title": "Principal component analysis: a review and recent developments",
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"key": "kamimoto2023celloracle",
"title": "Dissecting cell identity via network inference and in silico gene perturbation",
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"title": "Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion",
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"title": "Flow Matching for Generative Modeling",
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"key": "lopez2018scvi",
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"title": "Decoupled Weight Decay Regularization",
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"key": "norman2019exploring",
"title": "Exploring genetic interaction manifolds constructed from rich single-cell phenotypes",
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"title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
"url": "https://arxiv.org/abs/1912.01703",
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"key": "pedregosa2011sklearn",
"title": "Scikit-learn: Machine Learning in Python",
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"key": "peidli2024scperturb",
"title": "scPerturb: harmonized single-cell perturbation data",
"url": "https://doi.org/10.1038/s41592-023-02144-y",
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"key": "replogle2020combinatorial",
"title": "Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing",
"url": "https://doi.org/10.1038/s41587-020-0470-y",
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"key": "replogle2022mapping",
"title": "Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq",
"url": "https://doi.org/10.1016/j.cell.2022.05.013",
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"key": "roohani2024predicting",
"title": "Predicting transcriptional outcomes of novel multigene perturbations with GEARS",
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"key": "song2021scorebased",
"title": "Score-Based Generative Modeling through Stochastic Differential Equations",
"url": "https://arxiv.org/abs/2011.13456",
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"key": "song2023consistency",
"title": "Consistency Models",
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"key": "song2024improved",
"title": "Improved Techniques for Training Consistency Models",
"url": "https://arxiv.org/abs/2310.14189",
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"key": "theodoris2023transfer",
"title": "Transfer learning enables predictions in network biology",
"url": "https://doi.org/10.1038/s41586-023-06139-9",
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"key": "wolf2018scanpy",
"title": "SCANPY: large-scale single-cell gene expression data analysis",
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"key": "yu2025perturbnet",
"title": "PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations",
"url": "https://doi.org/10.1038/s44320-025-00131-3",
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{
"key": "zaheer2017deepsets",
"title": "Deep Sets",
"url": "https://arxiv.org/abs/1703.06114",
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] |