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KSDKG: construction and application of knowledge graph for kidney stone disease based on biomedical literature and public databases

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Abstract

Purpose:

Kidney stone disease (KSD) is a common urological disorder with an increasing incidence worldwide. The extensive knowledge about KSD is dispersed across multiple databases, challenging the visualization and representation of its hierarchy and connections. This paper aims at constructing a disease-specific knowledge graph for KSD to enhance the effective utilization of knowledge by medical professionals and promote clinical research and discovery.

Methods:

Text parsing and semantic analysis were conducted on literature related to KSD from PubMed, with concept annotation based on biomedical ontology being utilized to generate semantic data in RDF format. Moreover, public databases were integrated to construct a large-scale knowledge graph for KSD. Additionally, case studies were carried out to demonstrate the practical utility of the developed knowledge graph.

Results:

We proposed and implemented a Kidney Stone Disease Knowledge Graph (KSDKG), covering more than 90 million triples. This graph comprised semantic data extracted from 29,174 articles, integrating available data from UMLS, SNOMED CT, MeSH, DrugBank and Microbe-Disease Knowledge Graph. Through the application of three cases, we retrieved and discovered information on microbes, drugs and diseases associated with KSD. The results illustrated that the KSDKG can integrate diverse medical knowledge and provide new clinical insights for identifying the underlying mechanisms of KSD.

Conclusion:

The KSDKG efficiently utilizes knowledge graph to reveal hidden knowledge associations, facilitating semantic search and response. As a blueprint for developing disease-specific knowledge graphs, it offers valuable contributions to medical research.

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Data availability

The data associated with this study are not publicly available at this time, as they are currently reserved for additional analyses. However, further details can be provided upon reasonable request, if needed.

Notes

  1. https://bioportal.bioontology.org/ontologies/CKDO.

  2. https://bioportal.bioontology.org/ontologies/KTAO.

  3. https://id.nlm.nih.gov/mesh/.

  4. https://www.nlm.nih.gov/research/umls/.

  5. https://www.nlm.nih.gov/healthit/snomedct/index.html.

  6. https://go.drugbank.com/.

  7. https://github.com/ccszbd/MDKG.

  8. https://www.ontotext.com/products/graphdb/.

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Acknowledgements

We sincerely express our gratitude to Professor Kewei Xu and Dr. Cong Lai from the Department of Urology at Sun Yat-sen Memorial Hospital, Sun Yat-sen University, for their assistance and professional guidance during the design and development of our works.

Funding

This work was supported by the Key Research and Development Program of China (2022YFC3601600), the Guangzhou Science and Technology Plan (202201011545), the National Natural Science Foundation of China (61876194), the Science and Technology Innovation Special Project of Guangdong Province (202011020004), and Fundamental Research Funds for the Central Universities, Sun Yat-Sen University (24xkjc025).

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Authors and Affiliations

  1. Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China

    Jianping Man, Yufei Shi, Zhensheng Hu, Rui Yang & Yi Zhou

  2. Knowledge Representation and Reasoning (KR &R) Group, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands

    Zhisheng Huang

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  1. Jianping Man
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Correspondence to Yi Zhou.

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Man, J., Shi, Y., Hu, Z. et al. KSDKG: construction and application of knowledge graph for kidney stone disease based on biomedical literature and public databases. Health Inf Sci Syst 12, 54 (2024). https://doi.org/10.1007/s13755-024-00309-3

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