ArticleGenomics & informatics2019
A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition.
Article in Genomics & informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Drug knowledge discovery via multi-task learning and pre-trained models.BMC medical informatics and decision making · 2021Article
- A Novel Metric to Quantify the Effect of Pathway Enrichment Evaluation With Respect to Biomedical Text-Mined Terms: Development and Feasibility Study.JMIR medical informatics · 2021Article
- A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets.BMC medical informatics and decision making · 2020Article
- Hybrid phenotype mining method for investigating off-target protein and underlying side effects of anti-tumor immunotherapy.BMC medical informatics and decision making · 2020Article
- HPO-Shuffle: an associated gene prioritization strategy and its application in drug repurposing for the treatment of canine epilepsy.Bioscience reports · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different sources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.
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Registered trials
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