PerturbRx通过学习潜在转换来预测患者药物反应，在多个基准测试中表现出色，为药物反应预测提供了新的思路。
PerturbRx通过学习干预引起的潜在转换来预测患者药物反应，在TCGA和患者来源异种移植基准测试中，PerturbRx在评估的方法中实现了最强的综合预测性能。
PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction
Scarce data and tumor heterogeneity limit patient-level cancer treatment-response prediction. Existing approaches predict response from pretreatment molecular profiles and drug representations, without explicitly modeling the molecular changes expected under treatment. We propose PerturbRx, a treatment-conditioned representation learning framework that learns intervention-induced latent transitions and uses them as patient-drug response features. PerturbRx trains a drug- and dose-conditioned transition predictor from context-matched but cell-unpaired control and treated single-cell populations, then freezes and transfers the predictor to pretreatment patient profiles without requiring post-treatment measurements. The transition is combined with patient and drug representations to predict response. Across TCGA and patient-derived xenograft benchmarks, PerturbRx achieves the strongest aggregate predictive performance among the evaluated methods. These results support perturbation-pretrained latent transitions as useful representations for patient-level drug-response prediction.