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Inference of Functions, Roles, and Applications of Chemicals Using Linked Open Data and Ontologies

  • Tatsuya KushidaEmail author
  • Kouji Kozaki
  • Takahiro Kawamura
  • Yuka Tateisi
  • Yasunori Yamamoto
  • Toshihisa Takagi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11341)

Abstract

A simple method to efficiently collect reliable chemical information was studied for developing an ontological foundation. Even ChEBI, a major chemical ontology, which consists of approximately 90,000 chemicals and information about 1,000 biological and chemical roles, and applications, lacks information regarding the roles of most of the chemicals. NikkajiRDF, linked open data which provide information of approximately 3.5 million chemicals and 694 application examples, is also being developed. NikkajiRDF was integrated with Interlinking Ontology for Biological Concepts (IOBC), which includes 80,000 concepts, including information on a number of diseases and drugs. As a result, it was possible to infer new information on at least one of the 432 biological and chemical functions, applications and involvements with biological phenomena, including diseases to 5,038 chemicals using IOBC’s ontological structure. Furthermore, seven chemicals and drugs, which would be involved in 16 diseases, were discovered using knowledge graphs that were developed from IOBC.

Keywords

Chemical Disease Drug Inference Knowledge graph LOD Ontology RDF SPARQL 

Notes

Acknowledgments

This study was supported by an operating grant from the Japan Science and Technology Agency and JSPS KAKENHI Grant Number JP17H01789. A part of this study was progressed and discussed in Japan BioHackathon 2016 (BH16.12), which served as a research and development meeting. We are grateful to all participants who gave us their valuable advice and constructive comments.

References

  1. 1.
    Kimura, T., Kushida, T.: Openness of Nikkaji RDF data and integration of chemical information by Nikkaji acting as a hub. J. Inf. Process. Manag. 58(3), 204–212 (2015)CrossRefGoogle Scholar
  2. 2.
    NikkajiRDF Homepage in Life Science Database Archive. http://doi.org/10.18908/lsdba.nbdc01530–02-000. Accessed 8 Aug 2018
  3. 3.
    NikkajiRDF Homepage in NBDC RDF Portal. https://integbio.jp/rdf/?view=detail&id=nikkaji. Accessed 8 Aug 2018
  4. 4.
    Heller, S., McNaught, A., Stein, S., Tchekhovskoi, D., Pletnev, I.: InChI-the worldwide chemical structure identifier standard. J. Cheminform. 5(1), 7 (2013)CrossRefGoogle Scholar
  5. 5.
    Fu, G., Batchelor, C., Dumontier, M., Hastings, J., Willighagen, E., Bolton, E.: PubChemRDF: towards the semantic annotation of PubChem compound and substance databases. J. Cheminform. 7(1), 34 (2015)CrossRefGoogle Scholar
  6. 6.
    Willighagen, E.L., et al.: The ChEMBL database as linked open data. J. Cheminform. 5(1), 23 (2013)CrossRefGoogle Scholar
  7. 7.
    Hastings, J., Chepelev, L., Willighagen, E., Adams, N., Steinbeck, C., Dumontier, M.: The chemical information ontology: provenance and disambiguation for chemical data on the biological semantic web. PloS 6(10), e25513 (2011)CrossRefGoogle Scholar
  8. 8.
    Dumontier, M., et al.: The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery. J. Biomed. Semant. 5(1), 14 (2014)CrossRefGoogle Scholar
  9. 9.
    Chambers, J., et al.: UniChem: a unified chemical structure cross-referencing and identifier tracking system. J. Cheminform. 5(1), 3 (2013)CrossRefGoogle Scholar
  10. 10.
    NBDC RDF Portal SPARQL Endpoint. https://integbio.jp/rdf/sparql. Accessed 8 Aug 2018
  11. 11.
    Kushida, T., et al.: Efficient construction of a new ontology for life sciences by sub-classifying related terms in the Japan Science and Technology Agency Thesaurus. In: Proceedings of the 8th International Conference on Biomedical Ontology (ICBO 2017), vol. 2137, pp. 1–6. CEUR-WS.org, Newcastle (2017)Google Scholar
  12. 12.
    IOBC Homepage in BioPortal. http://purl.bioontology.org/ontology/IOBC. Accessed 8 Aug 2018
  13. 13.
    Noy, N.F., et al.: BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res. 37(suppl_2), W170–W173 (2009)CrossRefGoogle Scholar
  14. 14.
    IOBC SPARQL endpoint, http://lod.hozo.jp/repositories/IOBC. Accessed 8 Aug 2018
  15. 15.
    Hastings, J., et al.: The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res. 41(D1), D456–D463 (2013)CrossRefGoogle Scholar
  16. 16.
    Wikipedia. https://www.wikipedia.org/. Accessed 8 Aug 2018
  17. 17.
    Bizer, C., et al.: DBpedia-A crystallization point for the Web of Data. Web Semant.: Sci. Serv. Agents World Wide Web 7(3), 154–165 (2009)CrossRefGoogle Scholar
  18. 18.
    Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)CrossRefGoogle Scholar
  19. 19.
    Ertl, P., Patiny, L., Sander, T., Rufener, C., Zasso, M.: Wikipedia chemical structure explorer: substructure and similarity searching of molecules from Wikipedia. J. Cheminform. 7(1), 10 (2015)Google Scholar
  20. 20.
    DBpedia public SPARQL endpoint. https://dbpedia.org/sparql. Accessed 8 Aug 2018
  21. 21.
    Wikidata public SPARQL endpoint. https://query.wikidata.org/. Accessed 8 Aug 2018
  22. 22.
    ChEBI ontology files. ftp://ftp.ebi.ac.uk/pub/databases/chebi/ontology/. Accessed 8 Aug 2018Google Scholar
  23. 23.
    link2OtherDBs_basedOnUniChem of NikkajiRDF. http://doi.org/10.18908/lsdba.nbdc01530–02-006. Accessed 8 Aug 2018
  24. 24.
    SPARQL query result in Section 3.1. http://nikkaji-rdf.biosciencedbc.jp/download/quary24/chebi2nikkajiRDF/0,5000.html. Accessed 8 Aug 2018
  25. 25.
    Ghazvinian, A., Noy, N.F., Musen, M.A.: Creating mappings for ontologies in biomedicine: simple methods work. In: AMIA Annual Symposium Proceedings. American Medical Informatics Association, pp. 198–202 (2009)Google Scholar
  26. 26.
    SPARQL query result in Section 3.2. http://nikkaji-rdf.biosciencedbc.jp/download/quary25/reasoning_Inheritance/.html. Accessed 8 Aug 2018
  27. 27.
    Kushida, T., et al.: Refined JST thesaurus extended with data from other open life science data sources. In: Wang, Z., Turhan, A.-Y., Wang, K., Zhang, X. (eds.) JIST 2017. LNCS, vol. 10675, pp. 35–48. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70682-5_3CrossRefGoogle Scholar
  28. 28.
    Kushida, T., et al.: Refining JST thesaurus and discussing the effectiveness in life science research. In: Proceedings of 5th Intelligent Exploration of Semantic Data Workshop (IESD 2016, Co-located with ISWC 2016), pp. 1–14, Kobe (2016)Google Scholar
  29. 29.
    SPARQL query result in Section 3.3. http://nikkaji-rdf.biosciencedbc.jp/download/quary26/reasoning_knowledgeGraph/.html. Accessed 8 Aug 2018
  30. 30.
    Bodenreider, O., Nelson, S.J., Hole, W.T., Chang, H.F.: Beyond synonymy: exploiting the UMLS semantics in mapping vocabularies. In: Proceedings of AMIA Symposium, pp. 815–819 (1998)Google Scholar
  31. 31.
    Chepelev, L.L., Dumontier, M.: Semantic web integration of cheminformatics resources with the SADI framework. J. Cheminform. 3(1), 16 (2011)CrossRefGoogle Scholar
  32. 32.
    Alshahrani, M., Khan, M.A., Maddouri, O., Kinjo, A.R., Queralt-Rosinach, N., Hoehndorf, R.: Neuro-symbolic representation learning on biological knowledge graphs. Bioinformatics 33(17), 2723–2730 (2017)CrossRefGoogle Scholar
  33. 33.
    Wilkinson, M.D., et al.: The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.National Bioscience Database CenterJapan Science and Technology AgencyKawaguchiJapan
  2. 2.The Institute of Scientific and Industrial ResearchOsaka UniversitySuitaJapan
  3. 3.Japan Science and Technology AgencyKawaguchiJapan
  4. 4.Database Center for Life Science, Research Organization of Information and SystemsKashiwaJapan
  5. 5.Department of Biological Sciences, Graduate School of ScienceThe University of TokyoBunkyōJapan

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