spBART extends BART by modeling low-dimensional covariates parametrically for interpretability and high-dimensional epigenetic predictors nonparametrically, with a CV-based variable selection procedure, achieving AUC 0.96 on multiple myeloma epigenetic data.
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=
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PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.
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Semi-Parametric Bayesian Additive Regression Trees for Risk Prediction with High-Dimensional Epigenetic Signatures and Low-Dimensional Covariates
spBART extends BART by modeling low-dimensional covariates parametrically for interpretability and high-dimensional epigenetic predictors nonparametrically, with a CV-based variable selection procedure, achieving AUC 0.96 on multiple myeloma epigenetic data.
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What Makes a Representation Good for Single-Cell Perturbation Prediction?
PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.