背景:随着全球人口的老龄化和神经退行性疾病的易感性,阿尔茨海默病(AD)的新疗法迫切需要。用于药物发现和再利用的现有数据资源无法捕获疾病病因和对药物反应的核心关系。
目的:我们设计了阿尔茨海默氏症知识库(Alzheimer’sKnowledgeBase,Alzheimer’sKnowledgeBase,通过提供AD病因和候选治疗的全面知识表示来缓解这种需求。
方法:我们将AlzKB设计为大型,异构图知识库使用22种不同的外部数据源组装,描述不同组织级别的生物和制药实体(例如,化学品,基因,解剖学,和疾病)。AlzKB使用Web本体语言2本体来实施语义一致性并允许本体推理。我们提供AlzKB的公共版本,并允许用户运行和修改知识库的本地版本。
结果:AlzKB可在网上免费获得,目前包含118,902个实体,这些实体之间有1,309,527个关系。为了证明它的价值,我们使用图形数据科学和机器学习(1)基于AD与帕金森病的相似性提出新的治疗靶点,以及(2)重新调整可能治疗AD的现有药物的用途.对于每个用例,AlzKB恢复了已知的治疗关联,同时提出了生物学上合理的新关联。
结论:AlzKB是一种新的,公开可用的知识资源,使研究人员能够发现AD药物发现的复杂翻译关联。通过两个用例,我们表明,它是一个有价值的工具,提出新的治疗假设的基础上,公共生物医学知识。
BACKGROUND: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease\'s etiology and response to drugs.
OBJECTIVE: We designed the Alzheimer\'s Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics.
METHODS: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base.
RESULTS: AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones.
CONCLUSIONS: AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.