arXiv Machine Learning

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

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.

Hugging Face Trending Papers

Jul 6

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations

Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles. Preclinical transfer-learning models can simulate drug-induced expression changes but are often hard to interpret and unstable, whereas knowledge-graph methods provide mechanistic context yet remain static and fail to capture drug-induced transcriptomic perturbation dynamics.

rag diffusion benchmarks

arXiv AI

Jul 7

Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations

arXiv:2607. 04557v1 Announce Type: cross Abstract: Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles.

By Dongmin Bang, Sugyun An, Inyoung Sung, Ilho Yun, Sun Kim, Sangseon Lee

rag diffusion benchmarks

arXiv Machine Learning

Jun 2

Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models

arXiv:2606. 01461v1 Announce Type: new Abstract: Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes.

By Brenda Nogueira, Gisela A. Gonzalez-Montiel, Nitesh V. Chawla, Nuno Moniz

llms agents diffusion

arXiv Machine Learning

Jul 28

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling

arXiv:2607. 23447v1 Announce Type: new Abstract: Predicting cellular responses to unseen chemical perturbations is challenging due to unknown targets and mechanisms, high-dimensional expression responses, and limited experimental coverage of the large small-molecule design space.

By Yuche Gao, Jos\'e Miguel Hern\'andez-Lobato, Siyuan Guo

llms benchmarks

arXiv Machine Learning

Sep 11

scDEFT: A deep learning framework for drug-effect prediction and counterfactual reasoning

scDEFT is a deep learning framework that treats a drug as a conditioning operator on single‑cell representations, enabling prediction of drug‑induced state changes and responder status. The model learns drug‑conditioned cell latents via feature‑wise linear modulation, aggregates them over transcriptional neighborhoods, and ranks latent dimensions to identify genes distinguishing responders from non‑responders. Applied to a harmonized inflammatory bowel disease atlas of 1.16 million cells, scDEFT achieves 45% of the baseline‑to‑reproducibility ceiling in state‑change prediction and stratifies responders before treatment with an AUROC of 0.70, outperforming standard predictors.

By Murthy Devarakonda

arXiv Machine Learning

Sep 22

CLARITY: Medical World Model for Guiding Treatment Decisions by Simulating Context-Aware Disease Trajectories

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

llms benchmarks

arXiv Machine Learning

Aug 11

TRAPS: Treatment-Assignment Prediction via Pathway-informed Stratification

arXiv:2606. 09898v2 Announce Type: replace Abstract: Cancer treatment involves decisions across multiple clinical outcomes, yet pathway-informed deep learning models are typically evaluated in isolation, making their relative benefits unclear.

By Sujoy Banik, Sayantan Chakraborty, Boishakhi Das Toma, Zainab Ghafoor, Ushashi Bhattacharjee, Koushik Howlader, Tirtho Roy

benchmarks

arXiv AI

Jul 1

Modeling Cell-Cycle-Aware Single-Cell Drug Perturbation Responses

arXiv:2606. 30695v1 Announce Type: cross Abstract: Single-cell drug perturbation models should predict not only transcriptional response magnitude, but also whether a treatment alters the proliferative state of a cell.

By Dingping Zhao, Jie Lin

benchmarks

arXiv Machine Learning

Jul 7

CLARITY: Medical World Model for Guiding Treatment Decisions by Modeling Context-Aware Disease Trajectories in Latent Space

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

llms diffusion benchmarks

arXiv Machine Learning

Jul 16

Multimodal Empirical Bayes Variational Autoencoders for Joint Longitudinal and Time-to-Event Modeling

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

multimodal

arXiv Machine Learning

Jun 10

TRAPS: Therapeutic Response Analysis via Pathway-informed Stratification

arXiv:2606. 09898v1 Announce Type: new Abstract: Cancer treatment planning requires decisions across multiple clinical dimensions at once.

By Sujoy Banik, Sayantan Chakraborty, Boishakhi Das Toma, Zainab Ghafoor, Ushashi Bhattacharjee, Koushik Howlader, Tirtho Roy

benchmarks

arXiv Machine Learning

Aug 26

A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncology

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

rag multimodal benchmarks

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