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.
Keywords: Alzheimer; Alzheimer disease; artificial intelligence; drug discovery; drug repurposing; etiology; heterogeneous graph; knowledge base; knowledge graph; machine learning; open source; therapeutic discovery; therapeutic targets.
©Joseph D Romano, Van Truong, Rachit Kumar, Mythreye Venkatesan, Britney E Graham, Yun Hao, Nick Matsumoto, Xi Li, Zhiping Wang, Marylyn D Ritchie, Li Shen, Jason H Moore. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.04.2024.