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Machine learning for perturbational single-cell omics
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scGen predicts single-cell perturbation responses
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Cell type prioritization in single-cell data
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Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions
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muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data
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scCODA: A Bayesian model for compositional single-cell data analysis
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Milo: differential abundance testing on single-cell data using k-NN graphs
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