标题： 基于空间蛋白质组学的AI驱动虚拟组织用于临床诊断和生物医学发现 显示英文标题

标题： AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery

摘要： 空间蛋白质组学技术通过实现多种分子标记物及其空间组织的同步分析，改变了我们对复杂组织结构的理解。 这些数据的高维度、实验间不同的标记组合以及异质性的研究设计给计算分析带来了独特的挑战。 在此，我们提出了虚拟组织（VirTues），一个用于生物组织的基础模型框架，它在分子、细胞和组织尺度上都能运行。 VirTues在Transformer架构设计中引入了创新，包括一种能够捕捉空间和标记维度的新分词方案，以及能够扩展到高维多重数据同时保持可解释性的注意力机制。 VirTues在多样化的癌症和非癌症组织数据集上进行训练，无需任务特定的微调即可表现出强大的泛化能力，从而实现了跨研究分析和新标记整合。 作为一款通用模型，VirTues在临床诊断、生物学发现和患者病例检索任务中优于现有方法，同时提供了关于组织功能和疾病机制的见解。 显示英文摘要

摘要： Spatial proteomics technologies have transformed our understanding of complex tissue architectures by enabling simultaneous analysis of multiple molecular markers and their spatial organization. The high dimensionality of these data, varying marker combinations across experiments and heterogeneous study designs pose unique challenges for computational analysis. Here, we present Virtual Tissues (VirTues), a foundation model framework for biological tissues that operates across the molecular, cellular and tissue scale. VirTues introduces innovations in transformer architecture design, including a novel tokenization scheme that captures both spatial and marker dimensions, and attention mechanisms that scale to high-dimensional multiplex data while maintaining interpretability. Trained on diverse cancer and non-cancer tissue datasets, VirTues demonstrates strong generalization capabilities without task-specific fine-tuning, enabling cross-study analysis and novel marker integration. As a generalist model, VirTues outperforms existing approaches across clinical diagnostics, biological discovery and patient case retrieval tasks, while providing insights into tissue function and disease mechanisms.