ArticleBMC bioinformatics2021
Mining a stroke knowledge graph from literature.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- From narrative evidence to computable knowledge: a decision-relevant corpus for medicinal herb-disease relationships.Journal of biomedical semantics · 2026Article
- Annotated corpus for traditional formula-disease relationships in biomedical articles.Scientific data · 2025Article
- TCMRGAT: Relational graph attention networks for predicting stroke treatment efficacy of traditional Chinese medicine prescriptions.Frontiers in pharmacology · 2025Article
- The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data.Frontiers in pharmacology · 2023Article
- Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities.Journal of big data · 2023Article
- Systematic optimization of host-directed therapeutic targets and preclinical validation of repositioned antiviral drugs.Briefings in bioinformatics · 2022Article
- Correction to: Mining a stroke knowledge graph from literature.BMC bioinformatics · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.