Mining a stroke knowledge graph from literature

BMC Bioinformatics

Kai Lu¹  Wei Wang¹  Xi Yang²  Goran Nenadic³  Chengkun Wu⁴ 

[1] College of Computer, National University of Defence Technology, 410073, Changsha, China;College of Computer, National University of Defence Technology, 410073, Changsha, China;State Key Laboratory of High-Performance Computing, National University of Defence Technology, 410073, Changsha, China;Department of Computer Science, University of Manchester, M13 9PL, Manchester, UK;Department of Computer Science, University of Manchester, M13 9PL, Manchester, UK;State Key Laboratory of High-Performance Computing, National University of Defence Technology, 410073, Changsha, China;

关键词: Stroke;    Knowledge graph;    Biomedical text mining;    Traditional Chinese Medicine;   

DOI  :  10.1186/s12859-021-04292-4

来源: Springer

【 摘 要 】

BackgroundStroke has an acute onset and a high mortality rate, making it one of the most fatal diseases worldwide. Its underlying biology and treatments have been widely studied both in the “Western” biomedicine and the Traditional Chinese Medicine (TCM). However, these two approaches are often studied and reported in insolation, both in the literature and associated databases.ResultsTo aid research in finding effective prevention methods and treatments, we integrated knowledge from the literature and a number of databases (e.g. CID, TCMID, ETCM). We employed a suite of biomedical text mining (i.e. named-entity) approaches to identify mentions of genes, diseases, drugs, chemicals, symptoms, Chinese herbs and patent medicines, etc. in a large set of stroke papers from both biomedical and TCM domains. Then, using a combination of a rule-based approach with a pre-trained BioBERT model, we extracted and classified links and relationships among stroke-related entities as expressed in the literature. We construct StrokeKG, a knowledge graph includes almost 46 k nodes of nine types, and 157 k links of 30 types, connecting diseases, genes, symptoms, drugs, pathways, herbs, chemical, ingredients and patent medicine.ConclusionsOur Stroke-KG can provide practical and reliable stroke-related knowledge to help with stroke-related research like exploring new directions for stroke research and ideas for drug repurposing and discovery. We make StrokeKG freely available at http://114.115.208.144:7474/browser/ (Please click "Connect" directly) and the source structured data for stroke at https://github.com/yangxi1016/Stroke

【 授权许可】

CC BY