疾病本体论普查

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI:10.1146/ANNUREV-BIODATASCI-080917-013459
M. Haendel, J. McMurry, R. Relevo, C. Mungall, P. Robinson, C. Chute
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引用次数: 35

摘要

几个世纪以来,人类一直试图根据表型表现和可用的治疗方法对疾病进行分类。今天,存在着广泛的策略、资源和工具来对患者和疾病进行分类。本体论可以为沿着病因、发展、治疗和遗传学等不同轴线进行精确的分层和分类提供坚实的逻辑基础。疾病和表型本体论主要有四种使用方式:(a)知识的搜索、检索和注释;(b)数据整合和分析;(c)临床决策支持;以及(d)知识发现。计算推理可以连接现有知识,并产生关于药物靶点、预后预测或诊断的新见解和假设。在这篇综述中,我们研究了疾病和表型本体论的兴起,以及它们在生物医学中的不同表现和应用方式。
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A Census of Disease Ontologies
For centuries, humans have sought to classify diseases based on phenotypic presentation and available treatments. Today, a wide landscape of strategies, resources, and tools exist to classify patients and diseases. Ontologies can provide a robust foundation of logic for precise stratification and classification along diverse axes such as etiology, development, treatment, and genetics. Disease and phenotype ontologies are used in four primary ways: ( a) search, retrieval, and annotation of knowledge; ( b) data integration and analysis; ( c) clinical decision support; and ( d) knowledge discovery. Computational inference can connect existing knowledge and generate new insights and hypotheses about drug targets, prognosis prediction, or diagnosis. In this review, we examine the rise of disease and phenotype ontologies and the diverse ways they are represented and applied in biomedicine.
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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