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: 9977234
• 负责人: Lynn Marie Schriml
• 金额: $ 23.73万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9977234
• 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; 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.

人类疾病数据是确定药物靶点的生物医学研究的基石， 将遗传变异与表型联系起来，了解与 新的治疗方法和临床护理与生物医学研究的结合。因此，有一个 对人类疾病标准化表示的重大需求， 跨资源的概念，以支持计算工具的开发， 强大的数据分析和集成，并不断纳入有关我们的新见解， 了解疾病的发病机制。在过去的13年里，疾病本体论团队已经 一直致力于开发和应用基于病因学的人类疾病本体（DO） 并为生物医学界提供一个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。根据现有的资源统计数字， 已经注释了超过150，000个生物医学数据元素和引用。 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，以实现疾病的语义探索 病因学和对齐的疾病概念，代表36，711个临床词汇， 推荐信发表的临床和实验研究数量增加了10倍 每年（PubMed：临床研究），在过去的四十年中，有43，401篇PubMed文章， 与1975年的3，269人相比，2014年，我们对疾病的理解显着扩大 机制等我们在《释义及通则条例》（1.0）中确定了两个主要须予改善的范畴， 代表这一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内不同的疾病概况，以及（2） 代表复杂疾病的替代分类，以解决临床用例 治疗复杂疾病因此，我们建议开发DO（2.0），一种整合性疾病 疾病表征和注释的机制框架，目标是代表 不同的疾病特征，并改善现有的单一特征（DO 1.0）或混合特征 分类（ORDO，NCITesaurus，MonDO）。我们相信DO（2.0）将提供两个基因组 和临床研究社区的多功能系统，使研究人员能够 对常见的细胞、分子或 环境疾病机制。DO的使用将在临床上得到推广， 生物医学界通过高姿态的出版物，会议和讲习班。