The Human Disease Ontology: An integrated, mechanistic knowledge resource for biomedical research.

人类疾病本体论：生物医学研究的综合机械知识资源。

基本信息

批准号：
10697379
负责人：
Lynn Marie Schriml
金额：
$ 74.29万
依托单位：
UNIVERSITY OF MARYLAND BALTIMORE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-06 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10697379
关键词：
Acceleration Address Area Artificial Intelligence Attention Automobile Driving Biological Markers Biological Process Biomedical Research Classification Clinical Collaborations Communicable Diseases Communication Communities Complex Computer Analysis Data Data Aggregation Data Analyses Data Element Data Science Data Set Data Storage and Retrieval Databases Dental Development Differential Diagnosis Discipline of Nursing Disease Disease model Disparate Encyclopedia of DNA Elements Ensure Environmental Risk Factor Epigenetic Process Epitopes Etiology FAIR principles FlyBase Focus Groups Funding Gene Expression Genes Genetic Genetic Counseling Genomics Goals Health Human Immune Informatics Information Networks Information Resources Infrastructure International Knowledge Laboratories Language Learning Link Machine Learning Malignant Neoplasms Maps Medical Mental Health Mentors Metabolic Metadata Methods Modeling Multilingualism National Human Genome Research Institute Nomenclature Ontology Pathway interactions Pharmacy facility Play Positioning Attribute Production Productivity Proteomics Protocols documentation Publishing Quality Control RNA Rare Diseases Rat Genome Database Regulatory Element Research Research Personnel Resource Description Framework Resources Role Saccharomyces Semantics Standardization Strategic Planning Syndrome Technology Training Triage Update Variant Viral Vocabulary WormBase XenBase Zebrafish artificial intelligence method clinical care clinical training cost effective data integration data interoperability data management data repository data resource data reuse data sharing disease classification epigenomics genome database genome resource genomic data genomic variation human disease improved innovation interoperability knowledgebase meetings model organisms databases molecular subtypes mouse genome multidisciplinary open data outreach precision medicine programs public health relevance rare cancer social determinants social factors statistics success virtual

项目摘要

The 2020 NHGRI Strategic plan highlights the need for facilitating data and resource interoperability for advancing genomic research and promoting data reuse. The Human Disease Ontology (DO) Knowledgebase will provide a sustainable approach for linking the growing bodies of information related to core datasets across genomic and proteomic resources, as interoperable genomic resources enable precision medicine and knowledge dissemination. The DO plays a key role in disease knowledge organization, representation, and standardization, serving as a reference framework for multiscale biomedical data integration and analysis across thousands of clinical, biomedical and computational researchers and genomic resources around the world. Expanding the DO’s disease data and models for complex diseases will provide a comprehensive network of disease to disease relationships (DO’s diseasome) that represents a disease feature similarity network for clinical differential diagnosis exploration. We will deepen our knowledge and understanding of the interrelationships between genomics and the social and environmental factors that influence human health. We will deliver an increasingly comprehensive view of the roles and relationships of genomic variation, biomolecules, environmental drivers and regulatory elements on biological processes, and address the need for genomics training in the clinical workforce. We will build beyond the current set of coordinating genomic resources, offering increasingly automated approaches for aggregating and linking disease metadata in a scalable and cost-effective manner. The DO Knowledgebase will expand content, capacity to support the development of genomic data science and machine learning/artificial intelligence (ML/AI) methods. The overall goal of this proposal is to facilitate the linking of disease data via the DO’s diseaseome across broadly useful biomedical, clinical genomic, proteomic and epigenomic resources, to drive innovative machine learning research and to provide a resource for optimizing clinical care. The DO serves as the de facto standard for disease etiology across biomedical data repositories. Conservatively, based on available resource statistics, terms from the DO have been annotated to over 1.5 million biomedical data elements and citations, a 10x increase in the past 5 years. Our proposed aims position us well for providing a comprehensive disease resource for the genomic community. We have identified three main areas of improvement in the DO Knowledgebase to achieve our goals: (1) aggregating disease information across genomic resources, modeling complex disease and defining the disease environmental exposome; (2) automating the DO’s production workflow, enabling federated resource querying, producing a multi-lingual DO and dissemination of ML/AI ready datasets; (3) maintaining and expanding the DO’s collaborations, establishing a clinical training nosology program and convening topical focus groups.

2020 年 NHGRI 战略计划强调了促进数据和资源互操作性的必要性推进基因组研究并促进数据重用。人类疾病本体论 (DO) 知识库。将提供一种可持续的方法，将越来越多的与核心数据集相关的信息联系起来基因组和蛋白质组资源，因为可互操作的基因组资源使精准医学和 DO 在疾病知识的组织、表达和传播方面发挥着关键作用。标准化，作为多尺度生物医学数据集成和分析的参考框架世界各地数千名临床、生物医学和计算研究人员以及基因组资源。扩大 DO 的疾病数据和复杂疾病模型将提供一个全面的网络疾病与疾病之间的关系（DO 疾病），代表临床疾病特征相似性网络我们将加深对相互关系的认识和理解。我们将提供基因组学与影响人类健康的社会和环境因素之间的研究。对基因组变异、生物分子的作用和关系的日益全面的看法，生物过程的环境驱动因素和监管要素，并满足基因组学的需求我们将超越当前的协调基因组资源，提供临床人员培训。以可扩展且具有成本效益的方式聚合和链接疾病元数据的日益自动化的方法 DO 知识库将扩展内容和能力，以支持基因组数据的开发。科学和机器学习/人工智能（ML/AI）方法该提案的总体目标是通过 DO 的疾病组促进广泛有用的生物医学、临床基因组、蛋白质组和表观基因组资源，推动创新的机器学习研究并提供资源 DO 是生物医学数据中疾病病因学的事实上的标准。保守地，根据可用的资源统计数据，DO 中的术语已被注释为超过 150 万个生物医学数据元素和引用，在过去 5 年中增加了 10 倍。我们的目标是为基因组学界提供全面的疾病资源。确定了 DO 知识库中需要改进的三个主要领域以实现我们的目标：(1) 聚合跨基因组资源的疾病信息，建模复杂疾病并定义疾病环境暴露组；(2) 自动化 DO 的生产工作流程，实现联合资源查询，制作多语言 DO 并传播 ML/AI 就绪数据集 (3) 维护和扩展 DO 的合作，建立临床培训疾病学计划并召集专题焦点小组。