EVA: Towards a universal model of the immune system
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arXiv
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| Format: | Preprint |
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2026
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| author | Scienta Team Bandasack, Ethan Bouget, Vincent Bruley, Apolline Cattan, Yannis Claye, Charlotte Corney, Matthew Duquesne, Julien Kanbi, Karim El Fouché, Aziz Marschall, Pierre Strozzi, Francesco |
| author_facet | Scienta Team Bandasack, Ethan Bouget, Vincent Bruley, Apolline Cattan, Yannis Claye, Charlotte Corney, Matthew Duquesne, Julien Kanbi, Karim El Fouché, Aziz Marschall, Pierre Strozzi, Francesco |
| contents | The effective application of foundation models to translational research in immune-mediated diseases requires multimodal patient-level representations that can capture complex phenotypes emerging from multicellular interactions. Yet most current biological foundation models focus only on single-cell resolution and are evaluated on technical metrics often disconnected from actual drug development tasks and challenges. Here, we introduce EVA, the first cross-species, multimodal foundation model of immunology and inflammation, a therapeutic area where shared pathogenic mechanisms create unique opportunities for transfer learning. EVA harmonizes transcriptomics data across species, platforms, and resolutions, and integrates histology data to produce rich, unified patient representations. We establish clear scaling laws, demonstrating that increasing model size and compute translates to improvements in both pretraining and downstream tasks performance. We introduce a comprehensive evaluation suite of 39 tasks spanning the drug development pipeline: zero-shot target efficacy and gene function prediction for discovery, cross-species or cross-diseases molecular perturbations for preclinical development, and patient stratification with treatment response prediction or disease activity prediction for clinical trials applications. We benchmark EVA against several state-of-the-art biological foundation models and baselines on these tasks, and demonstrate state-of-the-art results on each task category. Using mechanistic interpretability, we further identify biological meaningful features, revealing intertwined representations across species and technologies. We release an open version of EVA for transcriptomics to accelerate research on immune-mediated diseases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10168 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EVA: Towards a universal model of the immune system Scienta Team Bandasack, Ethan Bouget, Vincent Bruley, Apolline Cattan, Yannis Claye, Charlotte Corney, Matthew Duquesne, Julien Kanbi, Karim El Fouché, Aziz Marschall, Pierre Strozzi, Francesco Quantitative Methods Artificial Intelligence Machine Learning The effective application of foundation models to translational research in immune-mediated diseases requires multimodal patient-level representations that can capture complex phenotypes emerging from multicellular interactions. Yet most current biological foundation models focus only on single-cell resolution and are evaluated on technical metrics often disconnected from actual drug development tasks and challenges. Here, we introduce EVA, the first cross-species, multimodal foundation model of immunology and inflammation, a therapeutic area where shared pathogenic mechanisms create unique opportunities for transfer learning. EVA harmonizes transcriptomics data across species, platforms, and resolutions, and integrates histology data to produce rich, unified patient representations. We establish clear scaling laws, demonstrating that increasing model size and compute translates to improvements in both pretraining and downstream tasks performance. We introduce a comprehensive evaluation suite of 39 tasks spanning the drug development pipeline: zero-shot target efficacy and gene function prediction for discovery, cross-species or cross-diseases molecular perturbations for preclinical development, and patient stratification with treatment response prediction or disease activity prediction for clinical trials applications. We benchmark EVA against several state-of-the-art biological foundation models and baselines on these tasks, and demonstrate state-of-the-art results on each task category. Using mechanistic interpretability, we further identify biological meaningful features, revealing intertwined representations across species and technologies. We release an open version of EVA for transcriptomics to accelerate research on immune-mediated diseases. |
| title | EVA: Towards a universal model of the immune system |
| topic | Quantitative Methods Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2602.10168 |