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Using Annotations from Controlled Vocabularies to Find Meaningful Associations

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Data Integration in the Life Sciences (DILS 2007)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4544))

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Abstract

This paper presents the LSLink (or Life Science Link) methodology that provides users with a set of tools to explore the rich Web of interconnected and annotated objects in multiple repositories, and to identify meaningful associations. Consider a physical link between objects in two repositories, where each of the objects is annotated with controlled vocabulary (CV) terms from two ontologies. Using a set of LSLink instances generated from a background dataset of knowledge we identify associations between pairs of CV terms that are potentially significant and may lead to new knowledge. We develop an approach based on the logarithm of the odds (LOD) to determine a confidence and support in the associations between pairs of CV terms. Using a case study of Entrez Gene objects annotated with GO terms linked to PubMed objects annotated with MeSH terms, we describe a user validation and analysis task to explore potentially significant associations.

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References

  1. Agrawal, R., et al.: Mining association rules between sets of items in large databases. SIGMOD Record 22(2), 207–216 (1993)

    Article  Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceeding of the 20th International Conference on Very Large Data Bases, pp. 487–499, San Francisco, CA, USA (September 1994)

    Google Scholar 

  3. Barnard, G.A.: Statistical inference. Journal of the Royal Statistical Society. Series B (Methodological) 11(2), 115–149 (1949)

    MathSciNet  Google Scholar 

  4. Blaschke, C., et al.: Evaluation of BioCreAtIvE assessment of task 2. BMC Bioinformatics 6(Suppl 1), S16 (2005)

    Article  Google Scholar 

  5. Camon, E., et al.: The Gene Ontology Annotation (GOA) Database: sharing knowledge in UniProt with Gene Ontology. Nucleic Acids Research 32(Database issue), D262–D266 (2004)

    Article  Google Scholar 

  6. Couto, F.M., et al.: Finding genomic ontology terms in text using evidence content. BMC Bioinformatics 6(Suppl 1), S21 (2005)

    Article  Google Scholar 

  7. Couto, F.M., et al.: GOAnnotator: linking protein GO annotations to evidence text. Journal of Biomedical Discovery and Collaboration 1(19) (December 20, 2006)

    Google Scholar 

  8. Current Semantic Types in the Unified Medical Language System (UMLS), http://www.nlm.nih.gov/research/umls/META3_current_semantic_types.ht ml

  9. Fiszman, M., et al.: Integrating a hypernymic proposition interpreter into a semantic processor for biomedical text. In: AMIA 2003 Annual Symposium, pp. 239–243, Washington, DC, USA (November 8-12, 2003)

    Google Scholar 

  10. Fujibuchi, W., et al.: DBGET/LinkDB: an integrated database retrieval system. In: Third Pacific Symposium on Biocomputing (PSB 1998), pp. 683–694, Maui, Hawaii, USA, (January 4-9, 1998)

    Google Scholar 

  11. Gene Ontology (GO), http://www.geneontology.org/

  12. Gene Ontology Annotation (GOA), http://www.ebi.ac.uk/GOA/

  13. Hamosh, A., et al.: Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Research 33(Database issue), D514–D517 (2005)

    Article  Google Scholar 

  14. Hoffmann, R., Valencia, A.: A gene network for navigating the literature. Nature Genetics 36(7), 664 (2004)

    Article  Google Scholar 

  15. Hristovski, D., et al.: Improving literature based discovery support by genetic knowledge integration. Studies in health technology and informatics 95, 68–73 (2003)

    Google Scholar 

  16. Kersey, P.J., et al.: Integr8: enhanced inter-operability of european molecular biology databases. Methods of Information in Medicine 42(2), 154–160 (2003)

    Google Scholar 

  17. Koike, A., Takagi, T.: Knowledge discovery based on an implicit and explicit conceptual network. Journal of the American Society for Information Science and Technology 58(1), 51–65 (2007)

    Article  Google Scholar 

  18. Korbel, J.O., et al.: Systematic association of genes to phenotypes by genome and literature mining. PLoS Biology 3(5) (April 5, 2005)

    Google Scholar 

  19. Lee, W.-J., Raschid, L., Vidal, M.-E.: A Generic, Flexible and Scalable Methodology to Enhance the Semantics of Links in Life Science Data Resources. Technical Report CS-TR-4809 (UMIACS-TR-2006-29), Univeristy of Maryland, (June 2006)

    Google Scholar 

  20. Maglott, D., et al.: Entrez Gene: gene-centered information at NCBI. Nucleic Acids Research 35(Database issue), D26–D31 (2007)

    Article  Google Scholar 

  21. Martin, A.C.: PDBSprotEC: a Web-accessible database linking PDB chains to EC numbers via SwissProt. Bioinformatics 20(6), 986–988 (2004)

    Article  Google Scholar 

  22. Medical Subject Headings (MeSH), http://www.nlm.nih.gov/mesh/meshhome.html

  23. Neumann, E.K., Quan, D.: Biodash: A semantic web dashboard for drug development. In: Eleventh Pacific Symposium on Biocomputing (PSB 2006), pp. 140–151, Maui, Hawaii, USA, (January 3-7, 2006)

    Google Scholar 

  24. Perez-Iratxeta, C., Bork, P., Andrade, M.A.: Association of genes to genetically inherited diseases using data mining. Nature Genetics 31(3), 316–319 (2002)

    Google Scholar 

  25. Ray, S., Craven, M.: Learning statistical models for annotating proteins with function information using biomedical text. BMC Bioinformatics 6(Suppl 1), S18 (2005)

    Article  Google Scholar 

  26. Rice, S.B., Nenadic, G., Stapley, B.J.: Mining protein function from text using term-based support vector machines. BMC Bioinformatics 6(Suppl 1), S22 (2005)

    Article  Google Scholar 

  27. Siadaty, M.S., Knausg, W.A.: Locating previously unknown patterns in data-mining results: a dual data- and knowledge- mining method. BMC Medical Informatics and Decision Making, 6(13) (March 7, 2006)

    Google Scholar 

  28. Srinivasan, P., Libbus, B.: Mining MEDLINE for implicit links between dietary substances and diseases. Bioinformatics 20(Supplement 1), i290–i296 (2004)

    Article  Google Scholar 

  29. Stanyon, C.A., et al.: A Drosophila protein-interaction map centered on cell-cycle regulators. Genome Biology 5(12), R96 (2004)

    Article  Google Scholar 

  30. Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), http://www.snomed.org/snomedct/

  31. Thomas, J., et al.: Automatic extraction of protein interactions from scientific abstracts. In: Fifth Pacific Symposium on Biocomputing (PSB 2000), pp. 538–549. Oahu, Hawaii, USA (2000)

    Google Scholar 

  32. Thorn, C.F., et al.: PharmGKB: the pharmacogenetics and pharmacogenomics knowledge base. Methods in Molecular Biology 311, 179–191 (2005)

    Google Scholar 

  33. Tiffin, N., et al.: Integration of text- and data-mining using ontologies successfully selects disease gene candidates. Nucleic Acids Research 33(5), 1544–1552 (2005)

    Article  Google Scholar 

  34. Wheeler, D.L., et al.: Database resources of the National Center for Biotechnology Information. Nucleic Acids Research 35(Database issue), D5–D12 (2007)

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Maryland, College Park, MD 20742, USA

    Woei-Jyh Lee & Louiqa Raschid

  2. The University of Iowa, Iowa City, IA 52242, USA

    Padmini Srinivasan

  3. Stanford University, Stanford, CA 94305, USA

    Nigam Shah, Daniel Rubin & Natasha Noy

Authors
  1. Woei-Jyh Lee
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  2. Louiqa Raschid
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  3. Padmini Srinivasan
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  4. Nigam Shah
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  5. Daniel Rubin
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  6. Natasha Noy
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Editor information

Sarah Cohen-Boulakia Val Tannen

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© 2007 Springer Berlin Heidelberg

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Lee, WJ., Raschid, L., Srinivasan, P., Shah, N., Rubin, D., Noy, N. (2007). Using Annotations from Controlled Vocabularies to Find Meaningful Associations. In: Cohen-Boulakia, S., Tannen, V. (eds) Data Integration in the Life Sciences. DILS 2007. Lecture Notes in Computer Science(), vol 4544. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73255-6_20

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  • DOI: https://doi.org/10.1007/978-3-540-73255-6_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73254-9

  • Online ISBN: 978-3-540-73255-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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