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

摘要文本 印第安纳州大学（MC-IU）的绘图组件将以规模a开发和实施 结合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图谱（Human Reference Atlas，HRA）。它将继续引领 ASCT+B表的构建（专家策划的解剖结构、细胞 类型，加上基于标准本体的生物标志物）和相关联的参考对象库 共同定义了进化中的人类参考地图集。我们将与 其它映射组件并入逐个细胞的基因参考（例如，方位角）和其他 细胞生物标记物参考和3D参考对象，因为它们变得可用。我们将 与蜂巢和其他团队一起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，以确保HRA符合 用户需求和支持驱动用例，推动生物医学研究和临床 实践与HUBMAP中的HIVE和组织数据生成团队密切合作， 除此之外，我们将定义和增量扩展用于查询的数据格式和本体， 整合和共享HRA数据。我们将结合现代机器学习， 人在环方法，以加快原始组织成像数据的速度， 转换成带注释和分割的组织图。先进的空间管理将是 用于跟踪组织数据中衍生的数百万个细胞，以支持 新的组织，并在HRA用户界面中实现高效的空间查询和探索。 我们将开发一个基于血管的通用坐标系统，以更好地捕捉人类 多样性和开发功能组织单位的计算模型，以连接解剖结构 来运作。

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.