ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Text Snippets to Corroborate Medical Relations: An Unsupervised Approach using a Knowledge Graph and Embeddings.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The trial behind it
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Who cites it
10 citing papers in PubMed.
- Applying prior knowledge of regulatory signaling to investigate macrophage cAMP dynamics during Mycobacterium tuberculosis infection.Scientific reports · 2026Article
- Leveraging dynamic stability to infer regulation in protein-protein interaction networks: A study of infectious vulnerability in COPD.PloS one · 2025Article
- A Computational Pipeline for Identifying Gene Regulatory Networks: A Case Study of Response to Exercise.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases.Annals of medicine · 2023Article
- A regulatory pathway model of neuropsychological disruption in Havana syndrome.Frontiers in psychiatry · 2023Article
- Old drugs, new tricks: leveraging known compounds to disrupt coronavirus-induced cytokine storm.NPJ systems biology and applications · 2022Article
- Proteomic Network Analysis of Bronchoalveolar Lavage Fluid in Ex-Smokers to Discover Implicated Protein Targets and Novel Drug Treatments for Chronic Obstructive Pulmonary Disease.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Mapping the network biology of metabolic response to stress in posttraumatic stress disorder and obesity.Frontiers in psychology · 2022Article
- An empirical meta-analysis of the life sciences linked open data on the web.Scientific data · 2021Article
- Development of a semi-structured, multifaceted, computer-aided questionnaire for outbreak investigation: e-Outbreak Platform.Biomedical journal · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knowledge graphs have been shown to significantly improve search results. Usually populated by subject matter experts, relations therein need to keep up to date with medical literature in order for search to remain relevant. Dynamically identifying text snippets in literature that confirm or deny knowledge graph triples is increasingly becoming the differentiator between trusted and untrusted medical decision support systems. This work describes our approach to mapping triples to medical text. A medical knowledge graph is used as a source of triples that are used to find matching sentences in reference text. Our unsupervised approach uses phrase embeddings and cosine similarity measures, and boosts candidate text snippets when certain key concepts exist. Using this approach, we can accurately map semantic relations within the medical knowledge graph to text snippets with a precision of 61.4% and recall of 86.3%. This method will be used to develop a novel application in the future to retrieve medical relations and corroborating snippets from medical text given a user query.
Identifiers
32477648PMC7233036What OpenQuestion 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 OpenQuestion graph.