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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

基本信息

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
项目类别：
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

项目摘要

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，