ArticleJournal of medical systems2019
KGDDS: A System for Drug-Drug Similarity Measure in Therapeutic Substitution based on Knowledge Graph Curation.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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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.
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Who cites it
7 citing papers in PubMed, 32 citations in OpenAlex.
- A systematic knowledge graph-based smart management method for operations: A case study of standardized management.Heliyon · 2023Article
- Analysis and identification of drug similarity through drug side effects and indications data.BMC medical informatics and decision making · 2023Article
- Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities.Journal of big data · 2023Article
- Leveraging Representation Learning for the Construction and Application of a Knowledge Graph for Traditional Chinese Medicine: Framework Development Study.JMIR medical informatics · 2022Article
- HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer Learning.Frontiers in genetics · 2022Article
- Knowledge-Based Biomedical Data Science.Annual review of biomedical data science · 2020Article
- A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development.JMIR medical informatics · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Measuring drug-drug similarity is important but challenging. Significant progresses have been made in drugs whose labeled training data is sufficient and available. However, handling data skewness and incompleteness with domain-specific knowledge graph, is still a relatively new territory and an under-explored prospect. In this paper, we present a system KGDDS for node-link-based bio-medical Knowledge Graph curation and visualization, aiding Drug-Drug Similarity measure. Specifically, we reuse existing knowledge bases to alleviate the difficulties in building a high-quality knowledge graph, ranging in size up to 7 million edges. Then we design a prediction model to explore the pharmacology features and knowledge graph features. Finally, we propose a user interaction model to allow the user to better understand the drug properties from a drug similarity perspective and gain insights that are not easily observable in individual drugs. Visual result demonstration and experimental results indicate that KGDDS can bridge the user/caregiver gap by facilitating antibiotics prescription knowledge, and has remarkable applicability, outperforming existing state-of-the-art drug similarity measures.
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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.