Development and validation of a computable knowledge framework for genomic medicine

基因组医学可计算知识框架的开发和验证

• 批准号: 10293672
• 负责人: Alex Handler Wagner
• 金额: $ 42.06万
• 依托单位: RESEARCH INST NATIONWIDE CHILDREN'S HOSP
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-08 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10293672
• 关键词: Clinical; Collection; Consumption; Data; Data Sources; Decision Making; Development; Diagnosis; Discipline; Education; Evaluation; Genome; Genomic medicine; Genomics; Growth; Health; Individual; Intuition; Knowledge; Left; Literature; Malignant Neoplasms; Modernization; Patients; Privatization; Prognosis; Reporting; Research; Research Activity; Resources; Risk; Role; Services; Standardization; System; Therapeutic; Time; Validation; Variant; Work; clinical care; clinical decision support; clinically relevant; clinically significant; combinatorial; cost; data ecosystem; data harmonization; design; genetic variant; genomic data; improved; innovation; interoperability; knowledge base; novel; research clinical testing; support tools; tool; tool development

Project Summary/Abstract Genomic medicine is the discipline of interpreting genomic information about an individual as part of their clinical care, for diagnosis, prognosis, or therapeutic decision-making. Integral to the practice of genome interpretation is the collection of multiple lines of evidence from knowledgebases to support or refute the clinical significance of evaluated variants. Modern clinical variant knowledgebases maintain literature and variant coverage that is mostly non-overlapping. This diversity of content causes a known problem in genome interpretation: analysts tasked with assembling a clinical variant report choose to spend considerable time navigating multiple resources and collating evidence, or risk missing critical information by selectively evaluating fewer resources. The resulting effort needed for an analyst to clinically interpret a variant list is known as the interpretation bottleneck, for its rate-limiting role in the clinical evaluation of patient genomes. Data integrators from public and private genomic medicine organizations work to alleviate this bottleneck by developing integrative clinical interpretation applications for use by genome analysts. As new knowledgebases are created, each of these public and private data integrators is left with the task of designing and maintaining another interface for each new resource, leading to combinatorial growth of data harmonization effort across the entire system. This approach is not scalable. This project will enable a transition to a scalable, interoperable, and federated genomic data ecosystem from the data integrators and knowledgebases already in existence today. To build an interoperable network of knowledgebases will require development and validation of a computable knowledge framework for genomic medicine. The framework will require the drafting of modern genomic knowledge standards, and development of the tools and services needed to support the implementation of those standards. These objectives will be carried out through coordination of several research activities with the Variant Interpretation for Cancer Consortium, ClinGen, and the Global Alliance for Genomics and Health. Research will involve conceptualization of genomic knowledge as precise computable concepts, designing schema for those concepts, developing framework support tools, and building intuitive user applications to leverage these advances. As a result of this research, new knowledgebases implementing this framework become immediately accessible to existing applications, while new applications and workflows implementing this framework are immediately able to leverage a wide breadth of data sources. In addition to these immediate benefits, the creation of a standardized, federated knowledge network reduces barriers for developing new products, spurring innovation towards novel applications for research, education, and clinical decision support tools.

项目摘要/摘要 基因组医学是解释有关个体的基因组信息的学科 用于诊断，预后或治疗决策的临床护理。基因组实践不可或缺 解释是从知识库中收集多种证据，以支持或驳斥 评估变体的临床意义。现代临床变异知识库维持文献和 变体覆盖范围主要是不重叠的。内容多样性在基因组中引起已知问题 解释：组装临床变体报告的任务分析师选择花费大量时间 通过选择性地导航多个资源和整理证据，或者有选择性地丢失关键信息 评估较少的资源。分析师在临床上解释变异列表所需的努力是 被称为解释瓶颈，其限制性在患者基因组的临床评估中的作用。 来自公共和私人基因组医学组织的数据集成商致力于减轻这种瓶颈 开发基因组分析师使用的综合临床解释应用。作为新知识库 创建了这些公共数据集成商的每个公共和私人数据集成商的任务 每个新资源的另一个接口，从而导致数据协调工作的组合增长 整个系统。这种方法是不可扩展的。 该项目将使从 数据集成商和知识库当今已经存在。建立一个可互操作的网络 知识库将需要开发和验证基因组的可计算知识框架 药品。该框架将需要制定现代基因组知识标准和发展 支持这些标准的实施所需的工具和服务。这些目标将是 通过与癌症的变异解释的几项研究活动协调进行 财团，克林根和全球基因组和健康联盟。研究将涉及 将基因组知识概念化为精确的可计算概念，为这些知识设计模式 概念，开发框架支持工具以及构建直观用户应用程序以利用这些应用程序 进步。 这项研究的结果是，实施此框架的新知识库立即成为 现有应用程序可访问，而实施此框架的新应用程序和工作流是 立即能够利用广泛的数据源。除了这些直接的好处， 建立标准化的联合知识网络可减少开发新产品的障碍， 促进了对研究，教育和临床决策支持工具的新颖应用的创新。