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将疾病图谱转化为重量级本体:一般方法及其在阿尔茨海默病中的应用。

Converting disease maps into heavyweight ontologies: general methodology and application to Alzheimer's disease.

机构信息

Inria Paris, Aramis Project-Team, Paris 75013, France.

Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.

出版信息

Database (Oxford). 2021 Feb 16;2021. doi: 10.1093/database/baab004.

DOI:10.1093/database/baab004
PMID:33590873
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7937031/
Abstract

Omics technologies offer great promises for improving our understanding of diseases. The integration and interpretation of such data pose major challenges, calling for adequate knowledge models. Disease maps provide curated knowledge about disorders' pathophysiology at the molecular level adapted to omics measurements. However, the expressiveness of disease maps could be increased to help in avoiding ambiguities and misinterpretations and to reinforce their interoperability with other knowledge resources. Ontology is an adequate framework to overcome this limitation, through their axiomatic definitions and logical reasoning properties. We introduce the Disease Map Ontology (DMO), an ontological upper model based on systems biology terms. We then propose to apply DMO to Alzheimer's disease (AD). Specifically, we use it to drive the conversion of AlzPathway, a disease map devoted to AD, into a formal ontology: Alzheimer DMO. We demonstrate that it allows one to deal with issues related to redundancy, naming, consistency, process classification and pathway relationships. Furthermore, we show that it can store and manage multi-omics data. Finally, we expand the model using elements from other resources, such as clinical features contained in the AD Ontology, resulting in an enriched model called ADMO-plus. The current versions of DMO, ADMO and ADMO-plus are freely available at http://bioportal.bioontology.org/ontologies/ADMO.

摘要

组学技术为增进我们对疾病的认识提供了巨大的潜力。此类数据的整合和解释带来了重大挑战,需要有足够的知识模型。疾病图谱提供了针对分子水平上疾病病理生理学的经过策展的知识,这些知识适用于组学测量。然而,疾病图谱的表达能力可以得到增强,以帮助避免歧义与误解,并加强其与其他知识资源的互操作性。本体论是通过其公理定义和逻辑推理特性来克服这一限制的合适框架。我们引入了疾病图谱本体(DMO),这是一个基于系统生物学术语的本体论上层模型。然后,我们提议将 DMO 应用于阿尔茨海默病(AD)。具体来说,我们使用它来驱动致力于 AD 的疾病图谱 AlzPathway 转换为正式的本体:Alzheimer DMO。我们证明,它允许人们处理与冗余、命名、一致性、过程分类和途径关系相关的问题。此外,我们还展示了它可以存储和管理多组学数据。最后,我们使用来自其他资源的元素扩展模型,例如包含在 AD 本体中的临床特征,从而得到一个名为 ADMO-plus 的丰富模型。当前版本的 DMO、ADMO 和 ADMO-plus 可在 http://bioportal.bioontology.org/ontologies/ADMO 上免费获取。

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Ontological Model in the Identification of Emotional Aspects in Alzheimer Patients.用于识别阿尔茨海默病患者情感方面的本体模型
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Efficacy and Safety of Lanabecestat for Treatment of Early and Mild Alzheimer Disease: The AMARANTH and DAYBREAK-ALZ Randomized Clinical Trials.Lanabecestat 治疗早期和轻度阿尔茨海默病的疗效和安全性:AMARANTH 和 DAYBREAK-ALZ 随机临床试验。
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BioModels-15 years of sharing computational models in life science.生物模型-15 年的生命科学计算模型共享经验。
Nucleic Acids Res. 2020 Jan 8;48(D1):D407-D415. doi: 10.1093/nar/gkz1055.
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Systems Biology Graphical Notation: Process Description language Level 1 Version 2.0.系统生物学图形符号:过程描述语言第1级版本2.0。
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New models of atherosclerosis and multi-drug therapeutic interventions.动脉粥样硬化新模型与多药物治疗干预。
Bioinformatics. 2019 Jul 15;35(14):2449-2457. doi: 10.1093/bioinformatics/bty980.
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STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.STRING v11:具有增强覆盖范围的蛋白质-蛋白质相互作用网络,支持在全基因组实验数据集的功能发现。
Nucleic Acids Res. 2019 Jan 8;47(D1):D607-D613. doi: 10.1093/nar/gky1131.
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The Gene Ontology Resource: 20 years and still GOing strong.《基因本体论资源:20 年,持续强大》
Nucleic Acids Res. 2019 Jan 8;47(D1):D330-D338. doi: 10.1093/nar/gky1055.
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Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms.系统医学疾病图谱:疾病机制的社区驱动综合表征
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