arXiv:2607. 17244v1 Announce Type: new Abstract: Longitudinal T cell receptor repertoires contain signals of clonal expansion, contraction, disappearance, and reappearance after immune perturbation.
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.
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...
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.
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.
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.
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.
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.
arXiv:2602. 17330v5 Announce Type: replace-cross Abstract: Comparative analysis of adaptive immune repertoires at population scale is hampered by two practical bottlenecks: the near-quadratic cost of pairwise affinity evaluations and dataset imbalances that obscure clinically important minority clonotypes.
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.
Loki-OT corrects lymphocyte mimicry by transferring region‑level tissue reasoning to individual cell predictions through Unbalanced Optimal Transport. It uses density priors from a pathology MLLM to guide ambiguous cell reassignment and distills the resulting transport plan into a lightweight MLP that learns context‑aware decision boundaries within pretrained cell‑foundation features. On the TCGA‑BRCA cohort, Loki‑OT outperformed a fully supervised PanopTILs classifier in patient‑level MAE and improved F1 scores in epithelium‑rich mimicry tissues.
arXiv:2606. 30398v1 Announce Type: new Abstract: Accurately predicting the temporal evolution of clinical biomarkers is crucial for the early diagnosis and management of neurodegenerative diseases such as Alzheimer's disease.
arXiv:2606. 04994v1 Announce Type: new Abstract: Accurate computational prediction of T cell receptor (TCR) antigen specificity would transform the study of T cell biology and enable scalable immune engineering, yet existing models lack sufficient sensitivity and specificity for broad applications.