Integration of Protein Data Sources Through PO

  • Amandeep S. Sidhu
  • Tharam S. Dillon
  • Elizabeth Chang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4080)

Abstract

Resolving heterogeneity among various protein data sources is a crucial problem if we want to gain more information about proteomics process. Information from multiple protein databases like PDB, SCOP, and UniProt need to integrated to answer user queries. Issues of Semantic Heterogeneity haven’t been addressed so far in Protein Informatics. This paper outlines protein data source composition approach based on our existing work of Protein Ontology (PO). The proposed approach enables semi-automatic interoperation among heterogeneous protein data sources. The establishment of semantic interoperation over conceptual framework of PO enables us to get a better insight on how information can be integrated systematically and how queries can be composed. The semantic interoperation between protein data sources is based on semantic relationships between concepts of PO. No other such generalized semantic protein data interoperation framework has been considered so far.

Keywords

Disulphide MeSH Glean Napa 

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References

  1. 1.
    GO Consortium: Creating the Gene Ontology Resource: Design and Implementation. Genome Research 11, 1425–1433 (2001) Google Scholar
  2. 2.
    Nelson, S.J., Schopen, M., et al.: The MeSH Translation Maintenance System: Structure, Interface Design, and Implementation. In: Fieschi, M., et al. (eds.) Proceedings of the 11th World Congress on Medical Informatics, San Francisco, CA, September 7-11, 2004, pp. 67–69. IOS Press, Amsterdam (2004)Google Scholar
  3. 3.
    Sidhu, A.S., Dillon, T.S., et al.: Ontology for Data Integration in Protein Informatics. In: Ma, Z., Chen, J.Y. (eds.) Database Modeling in Biology: Practices and Challenges. Springer Science, Inc., New York (2006) (in press)Google Scholar
  4. 4.
    Sidhu, A.S., Dillon, T.S., et al.: Protein Ontology Project: 2006 Updates (Invited Paper). In: Zanasi, A., Brebbia, C.A., Ebecken, N.F.F. (eds.) Data Mining and Information Engineering 2006, Prague, Czech Republic, WIT Press, Southampton (2006)Google Scholar
  5. 5.
    Sidhu, A.S., Dillon, T.S., et al.: Ontological Foundation for Protein Data Models. In: First IFIP WG 2.12 & WG 12.4 International Workshop on Web Semantics (SWWS 2005). Conjunction with On The Move Federated Conferences (OTM 2005), Agia Napa, Cyprus. LNCS. Springer, Heidelberg (2005)Google Scholar
  6. 6.
    Sidhu, A.S., Dillon, T.S., et al.: Protein Ontology: Vocabulary for Protein Data. In: 3rd IEEE International Conference on Information Technology and Applications (IEEE ICITA 2005), Sydney, vol. 1, pp. 465–469. IEEE CS Press, Los Alamitos (2005)CrossRefGoogle Scholar
  7. 7.
    GO Consortium, Lewis, S.E.: Gene Ontology: looking backwards and forwards. Genome Biology 6(1), 103.1–103.4 (2004)Google Scholar
  8. 8.
    Tan, H., Dillon, T.S., Hadzic, F., Chang, E., Feng, L.: IMB3-miner: Mining induced/Embedded subtrees by constraining the level of embedding. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS, vol. 3918, pp. 450–461. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Amandeep S. Sidhu
    • 1
  • Tharam S. Dillon
    • 1
  • Elizabeth Chang
    • 2
  1. 1.Faculty of Information TechnologyUniversity of TechnologySydneyAustralia
  2. 2.School of Information SystemsCurtin University of Technical UniversityPerthAustralia

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