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.
By Yoshitaka Inoue, Minoh Jeong, Alfred Hero, Rui Kuang, Augustin Luna
arXiv:2512.08029v4 Announce Type: replace
Abstract: Clinical decision-making in oncology requires forecasting how disease evolves under treatment, yet most AI systems remain static predictors that ca...
By Tianxingjian Ding, Yuanhao Zou, Chen Chen, Mubarak Shah, Yu Tian
arXiv:2606. 05797v1 Announce Type: new Abstract: Longitudinal treatment decisions require predicting potential outcomes under future treatment sequences in the presence of time-varying confounding, heterogeneous patient dynamics, and limited domain-specific data.
By Amirhossein Zare, Amirhessam Zare, Herlock Rahimi, Reza Salarikia, Mohammad Kashkooli
arXiv:2512. 08029v3 Announce Type: replace Abstract: Clinical decision-making in oncology requires predicting dynamic disease evolution, a task current static AI predictors cannot perform.
By Tianxingjian Ding, Yuanhao Zou, Chen Chen, Mubarak Shah, Yu Tian
arXiv:2608.24688v1 Announce Type: new
Abstract: Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal ob...
By Eugene Vorontsov, Yi Kan Wang, Alican Bozkurt, Adam Casson, Ludmila Tydlitatova, Michal Zelechowski, Ezra E. W. Cohen, Jyoti D. Patel, Max Banaszak, Caitlin McWilliams, Shane Colley, Kate Sasser, Ryan Fukushima, Eric Lefkofsky, Razik Yousfi, Siqi Liu
arXiv:2608. 08288v1 Announce Type: new Abstract: Estimating counterfactual outcomes over time from longitudinal observational data is central to clinical decision support.
By Abisoye Abidakun, Mingjun Zhong, Georgios Leontidis
arXiv:2607. 04912v1 Announce Type: cross Abstract: In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes.
By Johannes Kiechle, Richard Osuala, Daniel M. Lang, Stefan M. Fischer, Ivana Jan\'i\v{c}kov\'a, Karim Lekadir, Julia A. Schnabel, Jan C. Peeken
arXiv:2610.17059v1 Announce Type: new
Abstract: Clinical decisions depend on anticipating how a patient's condition will evolve as treatment is adjusted and new evidence becomes available. Existing m...
By Ziwen Kan, Jie Peng, Bo Yan, Davis Hobley, Zheng Zhang, Yu Tian, Tianlong Chen, Song Wang
Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the prediction of survival outcomes. The classical parametric...
arXiv:2610.07388v1 Announce Type: cross
Abstract: Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the predict...
By Barsha Halder, Jeffrey A. Thompson
arXiv:2607. 13984v1 Announce Type: cross Abstract: Longitudinal tumor measurements, dropout information, and genetic covariates provide complementary information about treatment response, but integrating these data sources within a single population modeling framework remains challenging.
By Anders Sj\"oberg, Nils Olsson, Marcus Baaz, Mats Jirstrand
arXiv:2608. 13518v1 Announce Type: new Abstract: Many clinical prediction models treat post-intervention outcomes as a one-step mapping from baseline measurements to a future endpoint.
By Yunsung Chung, Yingshuo Liu, Abboud F. Hassan, Han Feng, Mary M. Maleckar, Nassir Marrouche, Jihun Hamm