The study evaluated nine transcriptomic models—five bulk RNA‑seq and four single‑cell RNA‑seq—designed to predict response to immune checkpoint inhibitors. Across independent datasets, bulk models performed near chance while single‑cell models offered only modest gains, and pathway analyses revealed inconsistent biomarker signals. The results highlight the limited cross‑cohort robustness and biological consistency of current transcriptomic ICI predictors.
By Yuheng Liang, Lucy Chhuo, Ahmadreza Argha, Nona Farbehi, Lu Chen, Roohallah Alizadehsani, Mehdi Hosseinzadeh, Min Yang, Thantrira Porntaveetusm, Youqiong Ye, Hamid Alinejad-Rokny
The paper introduces a method for estimating the causal effects of T cell receptor (TCR) sequences on patient outcomes using observational TCR sequencing and clinical data. It corrects for unobserved confounders by leveraging the pre-selection TCR repertoire generated through V(D)J recombination as a natural experiment, and employs permutation‑invariant neural networks to scale to millions of sequences. The approach is validated on semisynthetic data and applied to COVID‑19 severity, identifying TCRs that are observed in patients, bind SARS‑CoV‑2 antigens in vitro, and positively influence clinical outcomes.
By Eli N. Weinstein, Elizabeth B. Wood, David M. Blei
JEPA-Anything is a domain‑agnostic framework that uses orthogonal predictive factorization (OPF) to decompose latent targets into complementary factors, learn them via dedicated pathways, and recombine them for shared prediction. The method is evaluated across seven diverse domains—vision, biology, clinical trajectories, control, molecular dynamics, physical fields, and weather—showing improvements on 10 dynamics tasks, reduced error on Interventional Pong, and lowest one‑step and 100‑step molecular errors among compared methods. Experimental validation includes a factor‑nominated biological intervention that succeeded in cell co‑cultures, organoids, tumor fragments, and mice, and latent orbital modes that recover the Keplerian scaling exponent.
By Taoyong Cui, Zhongyao Wang, Xinyue Xu, Weiyang Liu, Zhaochen Yu, Yuying Zhang, Qiang Gao, Mengyue Yang, Wanli Ouyang, Pheng Ann Heng, Yingcheng Wu, Zhenfei Yin, Ling Yang
arXiv:2607. 17244v1 Announce Type: new Abstract: Longitudinal T cell receptor repertoires contain signals of clonal expansion, contraction, disappearance, and reappearance after immune perturbation.
By Rong Fu, Yongtai Liu, Xiaowen Ma, Haoyu Zhao, Shuo Yin, Yiqing Lyu, Long Zhang, Wangyu Wu
arXiv:2607. 16262v1 Announce Type: cross Abstract: The acceleration of automated scientific discovery has been fundamentally bottlenecked by the epistemic gap between the semantic reasoning of large language models (LLMs) and the deterministic physics of mammalian biology.
By Christopher Baker, Tianyu Ren, Karen Rafferty, Hui Wang, Simon McDade
arXiv:2607. 19020v1 Announce Type: cross Abstract: Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet standard adaptation methods treat models as monolithic blocks, unable to distinguish stable patient physiology from shifting institutional practice.
By Fatema Ferdous Tamanna, K. M. Merajul Arefin, Md. Abdul Masud
A causal multi-modal AI model was developed to predict personalized chemosensitivity in breast cancer patients using routine pathology and clinical data. Trained on 9,141 patients from nine countries and validated on 1,994 patients from three countries, the model produced treatment-specific recurrence probabilities with near-perfect calibration and strong prognostic discrimination over 5- and 10-year horizons. It outperformed existing recurrence-score tests and could reduce chemotherapy prescriptions by 30% while maintaining recurrence-free rates, with predictive performance also transferring to non-breast cancers.
By Dhruva Biswas, Jeroen Berrevoets, Alec McClean, Linus Bao, Jungkyu Park, Ken G. Zeng, Joseph Cappadona, Cerise Tang, Chuwen Liu, Bartosz Machura, Yin Wu, Valerie Speirs, Hatem Soliman, Rohit Bhargava, Sheheryar Kabraji, Thaer Khoury, David Page, Brian Piening, Carlo Bifulco, Claudia Meurs, Pieter Westenend, Sylvie Chabaud, Jerome Lemonnier, Paul H. Cottu, Florence Dalenc, Fabrice Andre, Frederique Madeleine Penault-Llorca, Thomas Bachelot, Frederick Howard, Francisco J. Esteva, Kevin Kalinsky, Lajos Pusztai, Jan Witowski, Krzysztof J. Geras
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
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:2606. 05925v1 Announce Type: new Abstract: A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbations, disease progression and therapeutic intervention.
By Guangyu Wang, Jingkun Yue, Siqi Zhang, Yu Liu, Xiaoyu Wang, Mingyuan Meng, Changwei Ji, Zongbo Han, Yulin Wang, Yang Yue, Frank Fu, Ting Chen, Song Wu, Ziwei Liu, Jiangning Song, Ming Li, Gao Huang, Xiaohong Liu, Athanasios Vasilakos, Xingcai Zhang, Ping Zhang, Yong Li
arXiv:2502. 18864v2 Announce Type: replace Abstract: Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation.
By Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Petar Sirkovic, Artiom Myaskovsky, Grzegorz Glowaty, Felix Weissenberger, Alessio Orlandi, Dan Popovici, Anil Palepu, Keran Rong, Ryutaro Tanno, Khaled Saab, Fan Zhang, Jacob Blum, Andrew Carroll, Kavita Kulkarni, Nenad Tomasev, Dina Zverinski, Ivor Rendulic, Elahe Vedadi, Florian Hasler, Luka Rimanic, Marina Boia, Ivan Budiselic, Ben Feinstein, Mathias Bellaiche, Tom Sheffer, Jan Freyberg, Jeremy Ratcliff, Ottavia Bertolli, Katherine Chou, Avinatan Hassidim, Burak Gokturk, Amin Vahdat, Yuan Guan, Vikram Dhillon, Eeshit Dhaval Vaishnav, Byron Lee, Tiago R D Costa, Jos\'e R Penad\'es, Gary Peltz, Yossi Matias, James Manyika, Demis Hassabis, Yunhan Xu, Pushmeet Kohli, Annalisa Pawlosky, Alan Karthikesalingam, Vivek Natarajan