3D Multiscale Biomolecular Human Reference Atlas Construction, Visualization and Usage [4 of 5]

摘要文本 印第安纳大学(MC-IU)的地图绘制部分将在规模上开发和实施 结合了人类专业知识和机器学习算法的社会技术基础设施 构建、可视化和使用人类参考地图集(HRA)。它将继续引领世界 构建ASCT+B表(专家管理的、相关的解剖结构列表、细胞 类型，加上基于标准本体的生物标记)和相关联的引用对象库 这些共同定义了进化中的人类参考地图集。我们将与 其他映射组件，用于合并逐个细胞的参考(例如，方位)和其他 当它们可用时，逐个生物标记参考和3D参考对象。我们会工作的 与蜂巢和其他团队将地图集与实验数据和学术论文联系起来 证据。我们将与其他15个国际财团密切合作，确保HRA满足 用户需要并支持推动生物医学研究和临床的使用案例 练习一下。与HuBMAP和HIVE和组织数据生成团队密切合作 此外，我们还将定义并逐步扩展用于查询的数据格式和本体， 集成和共享HRA数据。我们将结合现代机器学习和 人在回路中的方法，以加快原始组织成像数据的速度 转换成带注释和分段的组织图。高级空间管理将是 用于跟踪从组织数据中派生的数百万细胞，以支持空间配准 新组织，并允许在HRA用户界面中进行有效的空间查询和探索。 我们将开发一个基于血管系统的通用坐标系，以更好地捕捉人类 多样性和开发功能组织单元的计算模型以连接解剖结构 才能发挥作用。

Abstract Text The Mapping Component at Indiana University (MC-IU) will develop and implement at scale a socio-technical infrastructure that combines human expertise and machine learning algorithms to construct, visualize, and use the Human Reference Atlas (HRA). It will continue to lead the construction of ASCT+B tables (expert-curated, connected lists of anatomical structures, cell types, plus biomarkers based on standard ontologies) and associated reference object libraries that together define the evolving Human Reference Atlas. We will collaborate closely with the other mapping component to incorporate cell-by-gene references (e.g., Azimuth) and other cell-by-biomarker references and 3D reference objects as they become available. We will work with the HIVE and other teams to link the atlas to experimental data and scholarly paper evidence. We will work closely with 15 other international consortia to ensure the HRA meets user needs and supports driving use cases that advance biomedical research and clinical practice. In close collaboration with the HIVE and tissue data generating teams in HuBMAP and beyond, we will define and incrementally extend data formats and ontologies for querying, integrating, and sharing HRA data. We will incorporate modern machine learning and human-in-the-loop approaches to accelerate the pace at which raw tissue imaging data is converted into annotated and segmented tissue maps. Advanced spatial management will be used to keep track of millions of cells derived in tissue data, to support spatial registration of new tissue, and to enable efficient spatial queries and exploration in the HRA user interfaces. We will develop a vasculature-based common coordinate system to better capture human diversity and develop computational models of functional tissue units to link anatomical structure to function.