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arXiv Machine Learning By Yoshitaka Inoue, Minoh Jeong, Alfred Hero, Rui Kuang, Augustin Luna

PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction

Read the original on arXiv Machine Learning →

PerturbRx is a treatment‑conditioned representation learning framework that learns latent transitions induced by drug interventions. It trains a drug‑ and dose‑conditioned transition predictor using control and treated single‑cell populations, then applies this predictor to pretreatment patient profiles to generate response features without needing post‑treatment data. On TCGA and patient‑derived xenograft benchmarks, PerturbRx outperforms other methods, demonstrating the value of perturbation‑pretrained latent transitions for patient‑level drug‑response prediction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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