Skip to main content
SpringerLink
Log in

Parallel Learning of Weighted Association Rules in Human Phenotype Ontology

  • Conference paper
  • First Online:
Euro-Par 2019: Parallel Processing Workshops (Euro-Par 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11997))

Included in the following conference series:

  • 1240 Accesses

Abstract

The Human Phenotype Ontology (HPO) is a standardized vocabulary of terms related to diseases. The importance and the specificity of HPO terms are estimated employing the Information Content (IC). Thus, the analysis of annotated data is a critical challenge for bioinformatics. There exist several approaches to support ontology curators in maintaining and analysing data. Among these, the use of Association Rules (AR) can improve the quality of annotations. In this paper, we present an algorithm for the parallel extraction of Weighted Association Rules (WAR) from HPO terms and annotations, able to face high dimension of data. Experiments performed on real and synthetic datasets show good speed-up and scalability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://hpo.jax.org/app/.

References

  1. Agapito, G., Cannataro, M., Guzzi, P.H., Milano, M.: Using GO-WAR for mining cross-ontology weighted association rules. Comput. Methods Programs Biomed. 120(2), 113–122 (2015). https://doi.org/10.1016/j.cmpb.2015.03.007. ISSN 0169-2607

    Article  Google Scholar 

  2. Agapito, G., Guzzi, P.H., Cannataro, M.: Parallel and distributed association rule mining in life science: a novel parallel algorithm to mine genomics data. Inf. Sci. (2018). https://doi.org/10.1016/j.ins.2018.07.055. ISSN 0020-0255

  3. Agapito, G., Milano, M., Guzzi, P.H., Cannataro, M.: Improving annotation quality in gene ontology by mining cross-ontology weighted association rules. In: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1–8. IEEE (2014). https://doi.org/10.1109/BIBM.2014.6999374

  4. Agapito, G., Milano, M., Guzzi, P.H., Cannataro, M.: Extracting cross-ontology weighted association rules from gene ontology annotations. IEEE/ACM Trans. Comput. Biol. Bioinf. 13(2), 197–208 (2015). https://doi.org/10.1109/TCBB.2015.2462348

    Article  Google Scholar 

  5. Agrawal, R., Imieli, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993). https://doi.org/10.1145/170036.170072

    Article  Google Scholar 

  6. Cai, C., Fu, A., Cheng, C., Kwong, W.: Mining association rules with weighted items. In: 1998 Database Engineering and Applications Symposium. Proceedings, IDEAS 1998, International, pp. 68–77 (1998). https://doi.org/10.1109/IDEAS.1998.694360

  7. Consortium, G.O., et al.: The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 32(Suppl. 1), D258–D261 (2004)

    Google Scholar 

  8. Faria, D., et al.: Mining go annotations for improving annotation consistency. PLoS ONE 7(7), e40519 (2012). https://doi.org/10.1371/journal.pone.0040519

    Article  Google Scholar 

  9. Flouris, G., Huang, Z., Pan, J.Z., Plexousakis, D., Wache, H.: Inconsistencies, negations and changes in ontologies. In: 1999 Proceedings of the National Conference on Artificial Intelligence, vol. 21, p. 1295 AAAI Press/MIT Press, Menlo Park/Cambridge (2006)

    Google Scholar 

  10. Gruber, T.: Encyclopedia of database systems. Ontology, pp. 1963–1965 (2009)

    Google Scholar 

  11. Hamosh, A., Scott, A.F., Amberger, J.S., Bocchini, C.A., McKusick, V.A.: Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33(Suppl. 1), D514–D517 (2005)

    Google Scholar 

  12. Harispe, S., Sanchez, D., Ranwez, S., Janaqi, S., Montmain, J.: A framework for unifying ontology-based semantic similarity measures: a study in the biomedical domain. J. Biomed. Inform. 48, 38–53 (2013)

    Article  Google Scholar 

  13. Hermjakob, H., et al.: The HUPO PSI’s molecular interaction format - a community standard for the representation of protein interaction data. Nat. Biotechnol. 22, 177–183 (2004). https://doi.org/10.1038/nbt926

    Article  Google Scholar 

  14. Kohler, S., et al.: Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am. J. Hum. Genet. 85(4), 457–464 (2009)

    Article  Google Scholar 

  15. Li, H., Wang, Y., Zhang, D., Zhang, M., Chang, E.Y.: PFP: parallel FP-Growth for query recommendation. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 107–114. ACM (2008)

    Google Scholar 

  16. Manda, P., McCarthy, F., Bridges, S.M.: Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new go relationships. J. Biomed. Inform. 46(5), 849–856 (2013)

    Article  Google Scholar 

  17. Manda, P., Ozkan, S., Wang, H., McCarthy, F., Bridges, S.M.: Cross-ontology multi-level association rule mining in the gene ontology. PLoS ONE 7(10), e47411 (2012)

    Article  Google Scholar 

  18. Milano, M., Agapito, G., Guzzi, P.H., Cannataro, M.: An experimental study of information content measurement of gene ontology terms. Int. J. Mach. Learn. Cybern. 9(3), 427–439 (2016). https://doi.org/10.1007/s13042-015-0482-y

    Article  Google Scholar 

  19. Peng, K., et al.: The disease and gene annotations (DGA): an annotation resource for human disease. Nucleic Acids Res. 41(D1), D553–D560 (2013). https://doi.org/10.1093/nar/gks1244

    Article  Google Scholar 

  20. Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: IJCAI, pp. 448–453 (1995), http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.5277

  21. Robinson, P.N., Kohler, S., Bauer, S., Seelow, D., Horn, D., Mundlos, S.: The human phenotype ontology: a tool for annotating and analyzing human hereditary disease. Am. J. Hum. Genet. 83(5), 610–615 (2008)

    Article  Google Scholar 

  22. Sanchez, D., Batet, M., Isern, D.: Ontology-based information content computation. Knowl.-Based Syst. 24(2), 297–303 (2011)

    Article  Google Scholar 

  23. Schriml, L.M., et al.: Disease ontology: a backbone for disease semantic integration. Nucleic Acids Res. 40(D1), D940–D946 (2012)

    Article  Google Scholar 

  24. Wang, W., Yang, J., Yu, P.S.: Efficient mining of weighted association rules (WAR). In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2000, pp. 270–274. ACM, New York (2000). https://doi.org/10.1145/347090.347149

  25. Yeh, I., Karp, P.D., Noy, N.F., Altman, R.B.: Knowledge acquisition, consistency checking and concurrency control for Gene Ontology (GO). Bioinformatics 19(2), 241–248 (2003)

    Article  Google Scholar 

  26. Zhou, Z., Wang, Y., Gu, J.: A new model of information content for semantic similarity in wordnet. In: 2008 Future Generation Communication and Networking Symposia, FGCNS 2008, vol. 3, pp. 85–89. IEEE (2008)

    Google Scholar 

Download references

Acknowledgments

This work has been partially funded by the following research project funded by the Calabrian Region: “Smart Electronic Invoices Accounting-SELINA CUP:J28C1700016006”.

Author information

Authors and Affiliations

  1. Magna Græcia University, 88100, Catanzaro, Italy

    Giuseppe Agapito, Mario Cannataro, Pietro Hiram Guzzi & Marianna Milano

Authors
  1. Giuseppe Agapito
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mario Cannataro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pietro Hiram Guzzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marianna Milano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giuseppe Agapito .

Editor information

Editors and Affiliations

  1. Gesellschaft für Wissenschaftliche Datenverarbeitung mbH, Göttingen, Germany

    Ulrich Schwardmann

  2. Gesellschaft für Wissenschaftliche Datenverarbeitung mbH, Göttingen, Germany

    Christian Boehme

  3. CiTIUS, Santiago de Compostela, Spain

    Dora B. Heras

  4. University of Rome "Tor Vergata", Rome, Italy

    Valeria Cardellini

  5. Inria Bordeaux Sud-Ouest, Talence, France

    Emmanuel Jeannot

  6. Engineering Sardegna, Cagliari, Italy

    Antonio Salis

  7. University of Turin, Torino, Italy

    Claudio Schifanella

  8. University College Dublin, Dublin, Ireland

    Ravi Reddy Manumachu

  9. DLR-AS, Göttingen, Germany

    Dieter Schwamborn

  10. University of Pisa, Pisa, Italy

    Laura Ricci

  11. Ajou University, Suwon, Korea (Republic of)

    Oh Sangyoon

  12. RRZE Friedrich-Alexander-Universität, Erlangen, Germany

    Thomas Gruber

  13. ICAR-CNR, Napoli, Italy

    Laura Antonelli

  14. Tennessee Technological University, Cookeville, TN, USA

    Stephen L. Scott

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Agapito, G., Cannataro, M., Guzzi, P.H., Milano, M. (2020). Parallel Learning of Weighted Association Rules in Human Phenotype Ontology. In: Schwardmann, U., et al. Euro-Par 2019: Parallel Processing Workshops. Euro-Par 2019. Lecture Notes in Computer Science(), vol 11997. Springer, Cham. https://doi.org/10.1007/978-3-030-48340-1_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-48340-1_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48339-5

  • Online ISBN: 978-3-030-48340-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation