Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Center of Excellence in Environmental Toxicology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
J Med Internet Res. 2024 Apr 18;26:e46777. doi: 10.2196/46777.
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.
We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics.
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.
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.
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.
随着全球人口老龄化和易患神经退行性疾病，迫切需要新的阿尔茨海默病 (AD) 治疗方法。现有的药物发现和再利用数据资源未能捕捉到与疾病病因和对药物反应相关的核心关系。
我们设计了阿尔茨海默病知识库 (AlzKB)，通过提供 AD 病因和候选治疗方法的综合知识表示来满足这一需求。
我们将 AlzKB 设计为一个大型的、异构的图形知识库，使用 22 个不同的外部数据源组装而成，这些数据源描述了不同组织层次的生物和药物实体（例如，化学物质、基因、解剖结构和疾病）。AlzKB 使用 Web 本体语言 2 本体来强制执行语义一致性并允许进行本体推理。我们提供了 AlzKB 的公共版本，并允许用户运行和修改知识库的本地版本。
AlzKB 可在网络上免费获取，目前包含 118902 个实体，这些实体之间有 1309527 种关系。为了展示其价值，我们使用图数据科学和机器学习来 (1) 根据 AD 与帕金森病的相似性提出新的治疗靶点，以及 (2) 重新利用可能治疗 AD 的现有药物。对于每个用例，AlzKB 都恢复了已知的治疗关联，同时提出了具有生物学合理性的新关联。
AlzKB 是一个新的、公开的知识资源，使研究人员能够发现 AD 药物发现的复杂转化关联。通过 2 个用例，我们表明它是基于公共生物医学知识提出新治疗假设的有价值工具。
