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PreMedOnto: A Computer Assisted Ontology for Precision Medicine

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Natural Language Processing and Information Systems (NLDB 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11608))

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Abstract

This paper proposes an ontology learning framework that combines text mining, information extraction and retrieval. The proposed model takes advantage of existing structured knowledge by reusing terms and concepts from other ontologies. We further apply the methodology to create a detailed ontology for the emerging precision medicine (PM) domain by collecting a corpus of relevant articles and mapping its frequent terms to existing concepts. The resulting ontology consists of 543 annotated classes. The ontology was also tested for effectiveness by applying two evaluation frameworks to validate its design and quality. The results demonstrate that the ontology learning system is able to capture and represent the semantics of the PM domain with high precision and significance. Moreover, the computer-assisted construction process reduced dependency on expert knowledge. The developed PreMedOnto ontology could be further used to enhance the potentials of other NLP applications in the PM domain.

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Notes

  1. 1.

    https://metamap.nlm.nih.gov/.

  2. 2.

    https://bioportal.bioontology.org/ontologies/NCIT.

  3. 3.

    https://bioportal.bioontology.org/ontologies/MESH.

  4. 4.

    https://bioportal.bioontology.org/ontologies/IOBC.

References

  1. Ali-Khan, S., Kowal, S., Luth, W., Gold, R., Bubela, T.: Terminology for personalized medicine: a systematic collection terminology for personalized medicine. Technical report (2016)

    Google Scholar 

  2. Alobaidi, M., Malik, K.M., Hussain, M.: Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain. Comput. Methods Programs Biomed. 165, 117–128 (2018). https://doi.org/10.1016/j.cmpb.2018.08.010

    Article  Google Scholar 

  3. Alobaidi, M., Malik, K.M., Sabra, S.: Linked open data-based framework for automatic biomedical ontology generation. BMC Bioinform. 19(1), 319 (2018). https://doi.org/10.1186/s12859-018-2339-3

    Article  Google Scholar 

  4. Amato, F., Santo, A.D., Moscato, V., Picariello, A., Serpico, D., Sperli, G.: A lexicon-grammar based methodology for ontology population for e-health applications. In: 2015 Ninth International Conference on Complex, Intelligent, and Software Intensive Systems. pp. 521–526. IEEE, July 2015. https://doi.org/10.1109/CISIS.2015.76

  5. Arguello Casteleiro, M., et al.: Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature. J. Biomed. Semant. 9(1), 13 (2018). https://doi.org/10.1186/s13326-018-0181-1

    Article  Google Scholar 

  6. Bontas, E.P., Mochol, M., Tolksdorf, R.: Case Studies on Ontology Reuse. Technical report

    Google Scholar 

  7. Buitelaar, P., Cimiano, P., Magnini, B.: Ontology learning from text: methods. Eval. Appl. (2005). https://doi.org/10.1162/coli.2006.32.4.569

    Article  Google Scholar 

  8. Cahyani, D.E., Wasito, I.: Automatic ontology construction using text corpora and ontology design patterns (ODPs) in Alzheimer’s disease. Jurnal Ilmu Komputer dan Informasi 10(2), 59 (2017). https://doi.org/10.21609/jiki.v10i2.374

    Article  Google Scholar 

  9. Dramé, K., et al.: Reuse of termino-ontological resources and text corpora for building a multilingual domain ontology: an application to Alzheimer’s disease. J. Biomed. Inform. 48, 171–182 (2014). https://doi.org/10.1016/J.JBI.2013.12.013

    Article  Google Scholar 

  10. Duque-ramos, A., Duque-ramos, A., Fernández-breis, J.T., Stevens, R., Aussenac-gilles, N.: OQuaRE: a SQuaRE-based approach for evaluating the quality of ontologies. J. Res. Pract. Inf. Technol. 43, 159 (2011)

    Google Scholar 

  11. Gao, M., Chen, F., Wang, R.: Improving Medical Ontology Based on Word Embedding (2018). https://doi.org/10.1145/3194480.3194490

  12. Gedzelman, S., Simonet, M., Bernhard, D., Diallo, G., Palmer, P.: Building an ontology of cardio-vascular diseases for concept-based information retrieval. In: Computers in Cardiology, 2005, pp. 255–258. IEEE (2005). https://doi.org/10.1109/CIC.2005.1588085

  13. Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquisition 5(2), 199–220 (1993). https://doi.org/10.1006/KNAC.1993.1008

    Article  Google Scholar 

  14. Jiménez-Ruiz, E., Cuenca Grau, B., Sattler, U., Schneider, T., Berlanga, R.: Safe and Economic Re-Use of Ontologies: A Logic-Based Methodology and Tool Support. Technical report

    Google Scholar 

  15. Kang, Y., Fink, J.C., Doerfler, R., Zhou, L.: Disease specific ontology of adverse events: ontology extension and adaptation for chronic kidney disease. Comput. Biol. Med. 101, 210–217 (2018). https://doi.org/10.1016/J.COMPBIOMED.2018.08.024

    Article  Google Scholar 

  16. Lossio-Ventura, J.A., Jonquet, C., Roche, M., Teisseire, M.: A Way to Automatically Enrich Biomedical Ontologies. https://doi.org/10.5441/002/edbt.2016.82

  17. Ochs, C., Perl, Y., Geller, J., Arabandi, S., Tudorache, T., Musen, M.A.: An empirical analysis of ontology reuse in BioPortal. J. Biomed. Inform. 71, 165–177 (2017). https://doi.org/10.1016/J.JBI.2017.05.021

    Article  Google Scholar 

  18. Poveda-Villalón, M., Carmen Suárez-Figueroa, M., Ángel García-Delgado, M., Gómez-Pérez, A.: OOPS! (OntOlogy Pitfall Scanner!): supporting ontology evaluation on-line. Technical report (2009)

    Google Scholar 

  19. Sánchez, D., Moreno, A.: Learning medical ontologies from the Web. Technical report

    Google Scholar 

  20. Shah, T., Rabhi, F., Ray, P., Taylor, K.: A guiding framework for ontology reuse in the biomedical domain. In: 2014 47th Hawaii International Conference on System Sciences, pp. 2878–2887. IEEE January 2014. https://doi.org/10.1109/HICSS.2014.360

  21. Xiang, Z., Courtot, M., Brinkman, R.R., Ruttenberg, A., He, Y.: OntoFox: web-based support for ontology reuse. BMC Res. Notes 3(1), 175 (2010). https://doi.org/10.1186/1756-0500-3-175

    Article  Google Scholar 

  22. Yates, L.R., et al.: The european society for medical oncology (ESMO) precision medicine glossary. Ann. Oncol. 29(1), 30–35 (2018). https://doi.org/10.1093/annonc/mdx707

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Engineering Department, College of Engineering, Arab Academy for Science, Technology, and Maritime Transport (AAST), Alexandria, 1029, Egypt

    Noha S. Tawfik

  2. Department of Information and Computing Sciences, Utrecht University, 3584 CC, Utrecht, The Netherlands

    Noha S. Tawfik & Marco R. Spruit

Authors
  1. Noha S. Tawfik
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  2. Marco R. Spruit
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Corresponding author

Correspondence to Noha S. Tawfik .

Editor information

Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Salford, Salford, UK

    Farid Meziane

  3. University of Salford, Salford, UK

    Sunil Vadera

  4. Oakland University, Rochester, MI, USA

    Vijayan Sugumaran

  5. CSE, University of Salford, Salford, UK

    Mohamad Saraee

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Tawfik, N.S., Spruit, M.R. (2019). PreMedOnto: A Computer Assisted Ontology for Precision Medicine . In: Métais, E., Meziane, F., Vadera, S., Sugumaran, V., Saraee, M. (eds) Natural Language Processing and Information Systems. NLDB 2019. Lecture Notes in Computer Science(), vol 11608. Springer, Cham. https://doi.org/10.1007/978-3-030-23281-8_28

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  • DOI: https://doi.org/10.1007/978-3-030-23281-8_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23280-1

  • Online ISBN: 978-3-030-23281-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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