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KGDDS: A System for Drug-Drug Similarity Measure in Therapeutic Substitution based on Knowledge Graph Curation

  • Systems-Level Quality Improvement
  • Published:
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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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Notes

  1. https://ckan.org/

  2. http://kw.fudan.edu.cn/apis/cndbpedia/

  3. https://www.drugbank.ca/

  4. disease-ontology.org/

  5. infectiousdiseaseontology.org/

  6. https://bioportal.bioontology.org/ontologies/IDODEN

  7. https://pypi.python.org/pypi/Owlready

  8. https://www.drugbank.ca/

  9. sideeffects.embl.de/

  10. https://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/NDFRT/

  11. https://www.ncbi.nlm.nih.gov/pubmed/

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (No.61602013), and the Shenzhen Fundamental Research Project (No. JCYJ20170818091546869, JCYJ20160330095313861). Min Yang was sponsored by CCF-Tencent Open Research Fund.

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Authors and Affiliations

  1. School of Electronics and Computer Engineering, Peking University (Shenzhen), 518055, Shenzhen, People’s Republic of China

    Ying Shen, Kaiqi Yuan, Jingchao Dai & Kai Lei

  2. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), 518055, Shenzhen, People’s Republic of China

    Buzhou Tang

  3. SIAT, Chinese Academy of Sciences, 518055, Shenzhen, People’s Republic of China

    Min Yang

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  1. Ying Shen
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  2. Kaiqi Yuan
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Correspondence to Kai Lei.

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Shen, Y., Yuan, K., Dai, J. et al. KGDDS: A System for Drug-Drug Similarity Measure in Therapeutic Substitution based on Knowledge Graph Curation. J Med Syst 43, 92 (2019). https://doi.org/10.1007/s10916-019-1182-z

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