DELPHAI predicts heterogeneous perturbation responses with learned cell fitness and gene-space retrieval

Modelling heterogeneous cellular responses to perturbation holds the promise of scalable in silico screening and mechanistic insight. However, mass conservation despite cell-type-specific depletion, and lossy projections from gene space to latent space, hinder performance of state-of-the-art methods. DELPHAI, with learned per-cell-fitness filtering out depleted cells and gene-space retrieval bypassing the latent bottleneck, outperforms all baseline methods across two benchmark frameworks and offers explainability with inferred cell-type-specific survival without any biological priors.