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

3D 多尺度生物分子人类参考图谱构建、可视化和使用 [4 of 5]

• 批准号: 10534308
• 负责人: KATY BORNER
• 金额: $ 182.74万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10534308
• 关键词: 3-Dimensional; Anatomy; Atlases; Automobile Driving; Biological Markers; Biomedical Research; Cells; Collaborations; Computer Models; Data; Ensure; Genes; Human; Human BioMolecular Atlas Program; Indiana; Infrastructure; International; Lead; Libraries; Link; Machine Learning; Modernization; Ontology; Paper; System; Text; Tissue imaging; Tissues; Universities; Visualization; Work; base; cell type; clinical practice; data format; human-in-the-loop; machine learning algorithm; tissue mapping

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.

抽象文本 印第安纳大学的测绘部门 (MC-IU) 将大规模开发和实施 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集 (HRA)。它将继续引领 构建 ASCT+B 表（专家策划的解剖结构、细胞的连接列表） 类型，加上基于标准本体的生物标志物）和相关的参考对象库 它们共同定义了不断发展的人类参考图集。我们将与 其他映射组件，用于合并逐个基因参考（例如，方位角）和其他 逐个生物标记参考和 3D 参考对象（当它们可用时）。我们将工作 与 HIVE 和其他团队合作，将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 用户需求和支持推动推动生物医学研究和临床的用例 实践。与 HuBMAP 中的 HIVE 和组织数据生成团队密切合作， 除此之外，我们将定义并逐步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和 人机交互方法可加快原始组织成像数据的处理速度 转换为带注释和分段的组织图。先进的空间管理将 用于跟踪组织数据中的数百万个细胞，以支持空间配准 新组织，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脉管系统的通用坐标系，以更好地捕捉人类 多样性并开发功能组织单元的计算模型以连接解剖结构 发挥作用。