arXiv:2602. 01051v5 Announce Type: replace Abstract: Repertoire-level analysis of T cell receptors offers a biologically grounded signal for disease detection and immune monitoring, yet practical deployment is impeded by label sparsity, cohort heterogeneity, and the computational burden of adapting large encoders to new tasks.
By Rong Fu, Muge Qi, Yang Li, Yabin Jin, Jiekai Wu, Chunlei Meng, Juntao Gao, Li Bao, Qi Zhao, Wei Luo, Youjin Wang, Simon Fong
arXiv:2609.14709v1 Announce Type: new
Abstract: Immune therapies act across cell-intrinsic programs, tissue ecosystems, and patient-specific immune states, yet most predictors address these scales se...
By Taoyong Cui, Xi Wang, Zonghang Li, Jinchao Ding, Lingsen You, Yuzhi Xu, Wanghan Xu, Fang Wu, Kejun Ying, Wanli Ouyang, Pheng Ann Heng, Ling Yang, Zhenfei Yin, Yingcheng Wu
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
arXiv:2608. 20104v1 Announce Type: cross Abstract: Artificial immune networks (AINs) are naturally memory-forming systems, but conventional visual AINs often rely on flattened vector affinity that ignores spatial structure.
By Siphesihle Sithungu
SCALE is a conditional transport model that treats cells as unordered sets to predict treated cell populations without requiring cell-level matching. It uses a shared set-aware encoder and a conditional DiT backbone to learn latent transport, enabling endpoint supervision that is directly delta-aligned. Across diverse perturbation types—including genetic, chemical, developmental, and immune—SCALE accurately recovers gene‑expression changes, response directions, and population structure, outperforming competing methods on CRISPR data and successfully prioritizing cytokines that elicit distinct immune responses.
By Shuizhou Chen, Lang Yu, Xueqin Lin, Xinjie Mao, Songming Zhang, Xinyu Gu, Hao Wu, Sheng Xu, Kedu Jin, Lei Bai, Quan Qian, Qin Chen, Qiang Gao, Siqi Sun, Zhangyang Gao
arXiv:2602. 06323v2 Announce Type: replace Abstract: Epidemiological forecasting from surveillance data is a hard problem and hybridizing mechanistic compartmental models with neural models is a natural direction.
By Yiqi Su, Ray Lee, Jiaming Cui, Naren Ramakrishnan