The Disease Ontology Project: mechanistic profiles of human disease for biomedical and clinical research

疾病本体项目：用于生物医学和临床研究的人类疾病的机制概况

• 批准号: 10204784
• 负责人: Lynn Marie Schriml
• 金额: $ 23.72万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-14 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10204784
• 关键词: Address; Anatomy; Animal Model; Area; Automobile Driving; Biomedical Research; Classification; Clinical; Clinical Medicine; Clinical Research; Communities; Complex; Coupling; Data; Data Analyses; Data Element; Development; Differential Diagnosis; Disease; Disease model; Disease susceptibility; Drug Targeting; Educational workshop; Environmental Health; Environmental Risk Factor; Etiology; Evolution; Funding; Genes; Genetic; Genetic Diseases; Genetic Variation; Genomics; Goals; Human Genetics; Knowledge; Link; Literature; Maryland; Medical Genetics; Medical center; Molecular; Nature; Ontology; Pathogenesis; Pathway interactions; Pharmaceutical Preparations; Phenotype; PubMed; Public Health; Publications; Publishing; Rare Diseases; Research; Research Personnel; Resources; Semantics; Standardization; Symptoms; System; Terminology; United States National Institutes of Health; Universities; Vocabulary; Work; base; bioinformatics resource; cancer genetics; cancer subtypes; clinical application; clinical care; clinical center; clinically relevant; comparative; computerized tools; data integration; disease classification; drug repurposing; experimental study; genetic resource; genetic variant; genome annotation; human disease; improved; insight; knowledge base; lens; medical schools; multimodality; novel; rare cancer; statistics; symposium; tool; transmission process; web interface; web site

Human disease data is a cornerstone of biomedical research for identifying drug targets, connecting genetic variations to phenotypes, understanding molecular pathways relevant to novel treatments and coupling clinical care and biomedical research. Consequently, there is a significant need for a standardized representation of human disease to connect disease concepts across resources, to support development of computational tools that will enable robust data analysis and integration and to continually incorporate new insights regarding our understanding of disease pathogenesis. For the past 13 years, the Disease Ontology team has been focusing on developing and applying an etiology based Human Disease Ontology (DO) and providing the biomedical community with a knowledgebase of integrated rare and common disease terms to support disease annotations for genomes, genes, genetic variants, associated biomedical data and literature. Conservatively, based on available resource statistics, terms from the DO have been annotated to over 150,000 biomedical data elements and citations. We have developed the DO, representing 6,782 human diseases and the DO web interface (http://www.disease-ontology.org) and RESTful API to enable semantic exploration of disease etiology and aligned disease concepts representing 36,711 clinical vocabulary cross- references. The 10-fold increase of the number of published clinical and experimental studies per year (PubMed: Clinical Study) in the past four decades, with 43,401 PubMed articles in 2014 compared to 3,269 in 1975, has markedly expanded our understanding of disease mechanisms. We have identified two main areas of improvement in the DO (1.0) necessary to represent this growing body of knowledge: (1) representing cellular, molecular and environmental mechanisms of disease as distinct disease profiles within the DO and (2) representing alternative classifications of complex disease in order to address clinical use cases for complex diseases. We thus propose to develop the DO (2.0), an integrative disease mechanism framework for disease characterization and annotation, with the goal to represent distinct disease profiles and improve upon the existing single profile (DO 1.0) or mixed profile classifications (ORDO, NCIthesaurus, MonDO). We believe DO (2.0) will provide both genomic and clinical research communities with a versatile system that will enable researchers to perform more accurate and comprehensive analysis of common cellular, molecular or environmental disease mechanisms. Utilization of the DO will be promoted in the clinical and biomedical communities through high profile publications, conferences and workshops.

人类疾病数据是识别药物靶标的生物医学研究的基石， 将遗传变异与表型联系起来，了解相关的分子途径 新的治疗方法以及临床护理和生物医学研究的结合。因此，有一个 迫切需要人类疾病的标准化表示来连接疾病 跨资源的概念，以支持计算工具的开发，从而使 强大的数据分析和集成，并不断融入有关我们的新见解 了解疾病发病机制。在过去的13年里，疾病本体团队已经 一直专注于开发和应用基于病因学的人类疾病本体论（DO） 并为生物医学界提供综合罕见和常见的知识库 支持基因组、基因、遗传变异、相关疾病注释的疾病术语 生物医学数据和文献。保守地说，根据可用的资源统计数据，术语 来自 DO 的数据已被注释为超过 150,000 个生物医学数据元素和引文。 我们开发了代表 6,782 种人类疾病的 DO 和 DO 网络界面 (http://www.disease-ontology.org) 和 RESTful API 实现疾病的语义探索 病因学和一致的疾病概念代表 36,711 个跨领域的临床词汇 参考。发表的临床和实验研究数量增加10倍 过去四十年中每年（PubMed：临床研究），有 43,401 篇 PubMed 文章 与 1975 年的 3,269 例相比，2014 年显着扩展了我们对疾病的了解 机制。我们已经确定了 DO (1.0) 中需要改进的两个主要领域 代表了这个不断增长的知识体系：（1）代表细胞、分子和 疾病的环境机制作为 DO 内不同的疾病特征，并且 (2) 代表复杂疾病的替代分类，以解决临床用例 对于复杂的疾病。因此，我们建议开发 DO (2.0)，一种综合疾病 疾病表征和注释的机制框架，目标是代表 不同的疾病概况并改进现有的单一概况（DO 1.0）或混合概况 分类（ORDO、NCIthesaurus、MonDO）。我们相信 DO (2.0) 将提供基因组 和临床研究社区拥有多功能系统，使研究人员能够 对常见的细胞、分子或 环境疾病机制。 DO的利用将在临床和临床中得到推广 通过高调的出版物、会议和研讨会来促进生物医学界的发展。