论文概要
研究领域: ML 作者: Yoshitaka Inoue, Minoh Jeong, Alfred Hero, Rui Kuang, Augustin Luna 发布时间: 2026-08-21 arXiv: 2608.21349
中文摘要
数据稀缺和肿瘤异质性限制了患者级别的癌症治疗反应预测。现有方法仅从治疗前分子谱和药物表征预测反应，未明确建模治疗下预期的分子变化。我们提出了PerturbRx，一种治疗条件化的表征学习框架，学习干预诱导的潜在转换并将其用作患者-药物反应特征。PerturbRx从上下文匹配但细胞未配对的处理和对照单细胞群体训练药物和剂量条件化的转换预测器，然后冻结并转移到治疗前患者谱，无需治疗后测量。该转换与患者和药物表征结合以预测反应。在TCGA和患者来源异种移植基准上，PerturbRx在评估方法中实现了最强的综合预测性能。这些结果支持扰动预训练的潜在转换作为患者级别药物反应预测的有用表征。
原文摘要
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 r…
— 自动采集于 2026-08-25
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