ArticleBMC bioinformatics2013
Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
- KGiA: Drug repurposing through disease-aware knowledge graph augmentation.Journal of biomedical informatics · 2025Article
- KGG: a fully automated workflow for creating disease-specific knowledge graphs.Bioinformatics (Oxford, England) · 2025Article
- Extracting Drug-Protein Relation from Literature Using Ensembles of Biomedical Transformers.Studies in health technology and informatics · 2024Article
- Repurposing ketamine to treat cocaine use disorder: integration of artificial intelligence-based prediction, expert evaluation, clinical corroboration and mechanism of action analyses.Addiction (Abingdon, England) · 2023Article
- From language models to large-scale food and biomedical knowledge graphs.Scientific reports · 2023Article
- Integrin β3-Mediated Cell Senescence Associates with Gut Inflammation and Intestinal Degeneration in Models of Alzheimer's Disease.International journal of molecular sciences · 2023Article
- Building a knowledge graph to enable precision medicine.Scientific data · 2023Article
- Drug repurposing for reducing the risk of cataract extraction in patients with diabetes mellitus: integration of artificial intelligence-based drug prediction and clinical corroboration.Frontiers in pharmacology · 2023Article
- Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.BioData mining · 2022Article
- KG-Predict: A knowledge graph computational framework for drug repurposing.Journal of biomedical informatics · 2022Article
- How can natural language processing help model informed drug development?: a review.JAMIA open · 2022Review
- Targets preliminary screening for the fresh natural drug molecule based on Cosine-correlation and similarity-comparison of local network.Journal of translational medicine · 2022Article
- PPDTS: Predicting potential drug-target interactions based on network similarity.IET systems biology · 2022Article
- A knowledge graph-based disease-gene prediction system using multi-relational graph convolution networks.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach.BMC bioinformatics · 2021Article
- Constructing a knowledge-based heterogeneous information graph for medical health status classification.Health information science and systems · 2020Article
- CoMNRank: An integrated approach to extract and prioritize human microbial metabolites from MEDLINE records.Journal of biomedical informatics · 2020Article
- Combining phenome-driven drug-target interaction prediction with patients' electronic health records-based clinical corroboration toward drug discovery.Bioinformatics (Oxford, England) · 2020Article
- A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development.JMIR medical informatics · 2020Article
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundA large-scale, highly accurate, machine-understandable drug-disease treatment relationship knowledge base is important for computational approaches to drug repurposing. The large body of published biomedical research articles and clinical case reports available on MEDLINE is a rich source of FDA-approved drug-disease indication as well as drug-repurposing knowledge that is crucial for applying FDA-approved drugs for new diseases. However, much of this information is buried in free text and not captured in any existing databases. The goal of this study is to extract a large number of accurate drug-disease treatment pairs from published literature.
resultsIn this study, we developed a simple but highly accurate pattern-learning approach to extract treatment-specific drug-disease pairs from 20 million biomedical abstracts available on MEDLINE. We extracted a total of 34,305 unique drug-disease treatment pairs, the majority of which are not included in existing structured databases. Our algorithm achieved a precision of 0.904 and a recall of 0.131 in extracting all pairs, and a precision of 0.904 and a recall of 0.842 in extracting frequent pairs. In addition, we have shown that the extracted pairs strongly correlate with both drug target genes and therapeutic classes, therefore may have high potential in drug discovery.
conclusionsWe demonstrated that our simple pattern-learning relationship extraction algorithm is able to accurately extract many drug-disease pairs from the free text of biomedical literature that are not captured in structured databases. The large-scale, accurate, machine-understandable drug-disease treatment knowledge base that is resultant of our study, in combination with pairs from structured databases, will have high potential in computational drug repurposing tasks.
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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.