An immune world model for multiscale forecasting and therapeutic hypothesis generation
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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.
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.
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.
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: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.
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.