An immune world model for multiscale forecasting and therapeutic hypothesis generation

• URL: http://arxiv.org/abs/2609.14709v1
• Date: Sun, 13 Sep 2026 18:06:18 GMT
• Title: An immune world model for multiscale forecasting and therapeutic hypothesis generation
• Abstract summary: We used a governed evolutionary AI Scientist to construct the Immune World Model.<n>The Immune World Model is an action-conditioned model that learns how interventions move immune states across cellular, tissue, and individual levels.<n>It provides a framework for multiscale immune simulation that connects AI Scientist-driven model construction, intervention forecasting, and the generation of prospectively testable therapeutic hypotheses.
• Score: 76.0289504908521
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Immune therapies act across cell-intrinsic programs, tissue ecosystems, and patient-specific immune states, yet most predictors address these scales separately. We used a governed evolutionary AI Scientist to construct the Immune World Model, an action-conditioned model that learns how interventions move immune states across cellular, tissue, and individual levels. The Immune World Model--building Scientist searched candidate architectures and workflows, and the resulting world model was frozen before independent confirmation. The frozen model generalized to unseen interventions and biological contexts, recovered intervention-specific cellular programs, integrated cell and tissue information to improve ecosystem and patient-response prediction, and forecast unseen perturbation combinations. Immune World Model--guided analysis then combined measured perturbations with cross-axis inference to nominate IL-36$γ$ plus SIRP$α$ inhibition as a complementary-axis therapeutic hypothesis, whereas a governed self-correction audit rejected every screened cytokine pair. The Immune World Model provides a framework for multiscale immune simulation that connects AI Scientist-driven model construction, intervention forecasting, and the generation of prospectively testable therapeutic hypotheses.

Related papers

• Towards World Models in Biomedical Research [77.59710966764466]
  We propose biomedical world models as a paradigm for AI-driven discovery.<n>These models learn latent representations of molecular, cellular, tissue and clinical states, together with intervention-conditioned dynamics.<n>We discuss how biomedical world models could function as data engines, environment simulators and scientific planning substrates across applications including virtual cells, organoids, virtual patients and surgical simulation.
  arXiv  Detail & Related papers  (2026-06-04T09:28:54Z)
• Prospective multi-pathogen disease forecasting using autonomous LLM-guided tree search [2.1770088624284547]
  We present an autonomous system to iteratively generate, evaluate, and optimize executable forecasting software.<n>The system autonomously discovered methodologically diverse models for influenza, COVID-19, and respiratory syncytial virus (RSV)<n>It consistently matched or outperformed the gold-standard, human-curated Centers for Disease Control and Prevention (CDC) hub ensembles out-of-sample.
  arXiv  Detail & Related papers  (2026-05-15T17:45:17Z)
• Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model [95.41752463487008]
  Parkinson's disease affects over ten million people worldwide.<n>Inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost.<n>We bridge this gap with a pretraining-finetuning framework to predict outcomes directly from resting-state fMRI.
  arXiv  Detail & Related papers  (2026-03-31T02:36:10Z)
• EVA: Towards a universal model of the immune system [0.18149976637753015]
  We introduce EVA, the first cross-species, multimodal foundation model of immunology and inflammation.<n> EVA harmonizes transcriptomics data across species, platforms, and resolutions, and integrates histology data to produce rich, unified patient representations.<n>We introduce a comprehensive evaluation suite of 39 tasks spanning the drug development pipeline.
  arXiv  Detail & Related papers  (2026-02-10T13:51:09Z)
• CLARITY: Medical World Model for Guiding Treatment Decisions by Modeling Context-Aware Disease Trajectories in Latent Space [49.74032713886216]
  CLARITY is a medical world model that forecasts disease evolution directly within a structured latent space.<n>It explicitly integrates time intervals (temporal context) and patient-specific data (clinical context) to model treatment-conditioned progression as a smooth, interpretable trajectory.
  arXiv  Detail & Related papers  (2025-12-08T20:42:10Z)
• ImmunoDiff: A Diffusion Model for Immunotherapy Response Prediction in Lung Cancer [10.797150801746957]
  Accurately predicting immunotherapy response in Non-Small Cell Lung Cancer (NSCLC) remains a critical unmet need.<n>Existing radiomics and deep learning-based predictive models rely primarily on pre-treatment imaging to predict categorical response outcomes.<n>This study introduces ImmunoDiff, an anatomy-aware diffusion model designed to synthesize post-treatment CT scans from baseline imaging while incorporating clinically relevant constraints.
  arXiv  Detail & Related papers  (2025-05-29T17:19:40Z)
• Harnessing Preference Optimisation in Protein LMs for Hit Maturation in Cell Therapy [0.5315454965484603]
  Cell and immunotherapy offer transformative potential for treating diseases like cancer and autoimmune disorders by modulating the immune system.<n>The development of these therapies is resource-intensive, with the majority of drug candidates failing to progress beyond laboratory testing.<n>Recent advances in machine learning have revolutionised areas such as protein engineering, applications in immunotherapy remain limited due to the scarcity of large-scale, standardised datasets and the complexity of cellular systems.
  arXiv  Detail & Related papers  (2024-12-02T11:21:58Z)
• Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder [6.352775857356592]
  We propose the Sparse Additive Mechanism Shift Variational Autoencoder, SAMS-VAE, to combine compositionality, disentanglement, and interpretability for perturbation models. SAMS-VAE models the latent state of a perturbed sample as the sum of a local latent variable capturing sample-specific variation and sparse global variables of latent intervention effects. We evaluate SAMS-VAE both and qualitatively on a range of tasks using two popular single cell sequencing datasets.
  arXiv  Detail & Related papers  (2023-11-05T23:37:31Z)
• Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance of Containment Measures [65.62256987706128]
  Compartmental epidemiological models categorize individuals based on their disease status. We propose an ABM architecture that combines an adapted SEIRD model with a decision-making model for citizens. We illustrate the designed model by examining the progression of SARS-CoV-2 infections in A Coruna, Spain.
  arXiv  Detail & Related papers  (2023-07-28T08:01:05Z)
• Conditional Generative Models for Simulation of EMG During Naturalistic Movements [45.698312905115955]
  We present a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy.
  arXiv  Detail & Related papers  (2022-11-03T14:49:02Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.