University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA.
Dalhousie University, Halifax, NS, Canada.
Nucleic Acids Res. 2019 Jan 8;47(D1):D955-D962. doi: 10.1093/nar/gky1032.
The Human Disease Ontology (DO) (http://www.disease-ontology.org), database has undergone significant expansion in the past three years. The DO disease classification includes specific formal semantic rules to express meaningful disease models and has expanded from a single asserted classification to include multiple-inferred mechanistic disease classifications, thus providing novel perspectives on related diseases. Expansion of disease terms, alternative anatomy, cell type and genetic disease classifications and workflow automation highlight the updates for the DO since 2015. The enhanced breadth and depth of the DO's knowledgebase has expanded the DO's utility for exploring the multi-etiology of human disease, thus improving the capture and communication of health-related data across biomedical databases, bioinformatics tools, genomic and cancer resources and demonstrated by a 6.6× growth in DO's user community since 2015. The DO's continual integration of human disease knowledge, evidenced by the more than 200 SVN/GitHub releases/revisions, since previously reported in our DO 2015 NAR paper, includes the addition of 2650 new disease terms, a 30% increase of textual definitions, and an expanding suite of disease classification hierarchies constructed through defined logical axioms.
人类疾病本体（DO）（http://www.disease-ontology.org）数据库在过去三年中经历了重大扩展。DO 疾病分类包括特定的正式语义规则，用于表达有意义的疾病模型，并从单一断言分类扩展到包括多种推断的机制疾病分类，从而为相关疾病提供了新的视角。疾病术语、替代解剖结构、细胞类型和遗传疾病分类的扩展以及工作流程自动化突出了自 2015 年以来 DO 的更新。DO 知识库的广度和深度的增强扩展了 DO 在探索人类疾病多病因方面的实用性，从而改善了跨生物医学数据库、生物信息学工具、基因组和癌症资源的健康相关数据的捕获和交流，并通过自 2015 年以来 DO 用户社区的增长 6.6 倍证明了这一点。自 2015 年我们在 DO 2015 NAR 论文中报告以来，DO 持续整合人类疾病知识，这体现在超过 200 次 SVN/GitHub 发布/修订中，包括增加 2650 个新疾病术语、30%的文本定义增加，以及通过定义逻辑公理构建的扩展疾病分类层次结构套件。
