用AI构建可模拟组织状态的虚拟组织，助力疾病机制研究
Building artificial intelligence virtual tissue (AIVT) for tissue state representation, feature prediction, and dynamic simulation
- 基于空间多模态数据训练的AI框架，学习组织的统一表征
- 能预测分子与形态特征，并模拟组织时空动态变化
- 适合生物医学建模、病理分析及药物研发人员使用
组织状态及其转变的建模对理解健康组织稳态和疾病中病理重塑至关重要。然而，传统计算建模方法难以捕捉组织作为空间有序、多尺度生物系统的复杂性。人工智能在表征复杂系统方面展现出显著能力，为刻画组织状态及其转变提供了新机遇。本文提出人工智能虚拟组织（AIVT）概念，这是一种基于空间多模态数据的AI框架，用于建模健康与疾病状态下的组织。AIVT旨在学习统一、空间解析且可动态操控的组织状态表征，实现组织状态表征与分析、分子与形态特征预测，以及时空组织动态模拟。文中阐述了AIVT的基本假设、核心能力、架构组件，以及数据与算法基础，为其作为人工智能驱动组织建模的框架提供系统性指导。
原文摘要 · Abstract (English)
Modeling tissue states and their transitions is essential for understanding tissue homeostasis in health and pathological remodeling in disease. However, conventional computational modeling approaches are inadequate to capture the complexity of tissues as spatially organized, multiscale biological systems. Artificial intelligence (AI) has shown a remarkable ability for representing intricate systems, creating new opportunities to characterize tissue states and their transitions. Here, we propose the concept of AI virtual tissue (AIVT), an AI framework grounded in spatial multimodal data for modeling tissues in health and disease. AIVT is designed to learn unified, spatially resolved, and dynamically manipulatable representations of tissue state, enabling tissue state representation and analysis, molecular and morphological feature prediction, and simulation of spatiotemporal tissue dynamics. We outline the fundamental assumptions, core capabilities, architectural components, as well as data and algorithm foundations of AIVT as a framework for AI-driven tissue modeling.
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