<ul><li>Learning the response of single-cells to various treatments offers great potential to enable targeted therapies.</li><li>Neural optimal transport (OT) has emerged as a methodological framework for analyzing unpaired single-cell data induced by cell destruction during data acquisition.</li><li>The Conditional Monge Gap is proposed as a method that learns OT maps conditionally on arbitrary covariates, such as time, drug treatment, drug dosage, or cell type.</li><li>The conditional models show promising generalization performance to unseen treatments, outperforming other models in capturing heterogeneity in the perturbed population.</li></ul>

Towards generalizable single-cell perturbation modeling via the Conditional Monge Gap

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