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Converting disease maps into heavyweight ontologies: general methodology and application to Alzheimer’s disease
Database: The Journal of Biological Databases and Curation ( IF 5.8 ) Pub Date : 2021-02-16 , DOI: 10.1093/database/baab004
Vincent Henry 1, 2, 3, 4, 5, 6 , Ivan Moszer 2, 3, 4, 5, 6 , Olivier Dameron 7 , Laura Vila Xicota 2, 3, 4, 5, 8 , Bruno Dubois 2, 3, 4, 5, 9 , Marie-Claude Potier 2, 3, 4, 5, 8 , Martin Hofmann-Apitius 10 , Olivier Colliot 1, 2, 3, 4, 5 ,
Affiliation  

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.


中文翻译:

将疾病地图转化为重量级本体:一般方法和在阿尔茨海默病中的应用

摘要
组学技术为提高我们对疾病的理解提供了巨大的希望。此类数据的整合和解释提出了重大挑战,需要足够的知识模型。疾病地图提供了适用于组学测量的分子水平上有关疾病病理生理学的精选知识。然而,可以增加疾病地图的表现力,以帮助避免歧义和误解,并加强它们与其他知识资源的互操作性。本体是一个足够的框架来克服这个限制,通过它们的公理定义和逻辑推理属性。我们介绍了疾病地图本体 (DMO),这是一种基于系统生物学术语的本体上层模型。然后我们建议将 DMO 应用于阿尔茨海默病 (AD)。具体来说,我们使用它来驱动 AlzPathway(一种专门用于 AD 的疾病地图)转换为正式本体:Alzheimer DMO。我们证明它允许人们处理与冗余、命名、一致性、过程分类和通路关系相关的问题。此外,我们表明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(例如 AD Ontology 中包含的临床特征)扩展模型,从而生成一个称为 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 免费获得。此外,我们表明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(例如 AD Ontology 中包含的临床特征)扩展模型,从而生成一个称为 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 免费获得。此外,我们表明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(例如 AD Ontology 中包含的临床特征)扩展模型,从而生成一个称为 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 免费获得。
更新日期:2021-03-03
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