多细胞生物
可解释性
生物
计算生物学
系统生物学
代表(政治)
计算机科学
钥匙(锁)
免疫系统
人工智能
传染病(医学专业)
鉴定(生物学)
完备性(序理论)
断言
生物医学
生物信息学
破译
人类疾病
信息流
理论计算机科学
协变变换
数据科学
生成语法
计算模型
认知科学
疾病
基因调控网络
作者
Kuan Pang,Yanay Rosen,Kasia Kedzierska,Ziyuan He,Abhe Rajagopal,Claire E Gustafson,Grace Huynh,Jure Leskovec,Kuan Pang,Yanay Rosen,Kasia Kedzierska,Ziyuan He,Abhe Rajagopal,Claire E Gustafson,Grace Huynh,Jure Leskovec
标识
DOI:10.1101/2025.11.24.685470
摘要
Biology emerges from interactions across physical scales, where molecular interactions drive cellular states, which in turn orchestrate multicellular tissue functions that collectively define health and disease. However, current computational models are often constrained to single scales in isolation, failing to integrate the biology that emerges from lower to higher levels. Here we present PULSAR (Patient Understanding Leveraging Single-cell universal Representation), a multi-scale and multicellular foundation model architecture that explicitly enables information flow from genes to cells to multicellular systems. Applied to the human peripheral immune system, PULSAR extracts a unified donor representation that supports rapid disease classification, biomarker prediction, and forecasting of future clinical events, such as Rheumatoid arthritis onset. As a generative model, PULSAR enables the simulation of cytokine perturbation response across physical resolutions, while its interpretability reveals the key cell types driving disease. Overall, PULSAR opens new avenues for precision medicine by enabling computational reasoning that connects molecular biology to clinical phenotypes.
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