دورية أكاديمية
The Alzheimer’s Knowledge Base: A Knowledge Graph for Alzheimer Disease Research
| العنوان: | The Alzheimer’s Knowledge Base: A Knowledge Graph for Alzheimer Disease Research |
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| المؤلفون: | 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 |
| المصدر: | Journal of Medical Internet Research, Vol 26, p e46777 (2024) |
| بيانات النشر: | JMIR Publications, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Public aspects of medicine |
| مصطلحات موضوعية: | Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270 |
| الوصف: | BackgroundAs 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. ObjectiveWe designed the Alzheimer’s Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. MethodsWe 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. ResultsAlzKB 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. ConclusionsAlzKB 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1438-8871 |
| Relation: | https://www.jmir.org/2024/1/e46777; https://doaj.org/toc/1438-8871 |
| DOI: | 10.2196/46777 |
| URL الوصول: | https://doaj.org/article/484c4a7497fd40b4bfb9252d73687d5c |
| رقم الأكسشن: | edsdoj.484c4a7497fd40b4bfb9252d73687d5c |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14388871 |
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| DOI: | 10.2196/46777 |