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

人类疾病数据是识别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 非常需要人类疾病的标准化表示法来将疾病联系起来 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 对疾病发病机制的认识。在过去的13年里，疾病本体论团队 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了一个综合稀有与常见的知识库 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，基于可用的资源统计数据、术语 从DO到超过150,000个生物医学数据元素和引用都有注释。 我们已经开发了代表6782种人类疾病的DO和DO网络界面 (http://www.disease-ontology.org)和RESTful API支持对疾病进行语义探索 病因学和统一的疾病概念代表36,711个临床词汇 参考文献。发表的临床和实验研究的数量增加了10倍 在过去的40年里，每年(PubMed：临床研究)有43,401篇PubMed文章发表在 与1975年的3269人相比，2014年显著扩大了我们对疾病的理解 机械装置。我们已经确定了DO(1.0)中需要改进的两个主要领域 代表这一不断增长的知识体系：(1)代表细胞、分子和 作为DO内不同疾病特征的环境疾病机制和(2) 代表复杂疾病的可选分类，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 疾病特征和注释的机制框架，目的是代表 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、NCI同义词库、MODO)。我们相信DO(2.0)将提供两种基因组 和临床研究社区，拥有一个通用的系统，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得到推广 通过高知名度的出版物、会议和研讨会，促进生物医学社区的发展。

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.