Abstract
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.
Abstract (translated)
将基础模型有效应用于自身免疫性疾病转化研究，需要多模态的患者层面表示，这些表示能够捕捉到复杂表型，而后者是由多细胞相互作用产生的。然而，目前大多数生物基础模型仅专注于单细胞解析，并且其评估指标通常与实际药物开发任务和挑战脱节。在这里，我们介绍了EVA——首个跨物种、多模态的免疫学和炎症基础模型，在这种治疗领域中共享的致病机制创造了独特的迁移学习机会。 EVA 跨越物种、平台和分辨率对转录组数据进行协调，并整合组织病理学数据以生成丰富统一的患者表示。我们确立了明确的扩展规律，表明增加模型大小和计算量可以提升预训练及下游任务性能。我们还提出了一套全面的任务评估工具集，涵盖药物开发流程中的39项任务：包括发现阶段的零样本靶点功效与基因功能预测、临床前研发阶段跨物种或跨疾病的分子扰动实验以及用于临床试验应用患者的分层治疗反应和疾病活动性预测。 我们在这些任务上对EVA与其他最先进的生物基础模型及基准进行了对比测试，并在每个任务类别中展示了最优结果。通过机制解释，我们进一步识别出具有生物学意义的特征，揭示了跨物种和技术交织表示的特点。我们将发布一个公开版本的EVA转录组学以加速自身免疫性疾病研究进程。
URL
https://arxiv.org/abs/2602.10168
