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Extraction of Constraints from Biological Data

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 224))

Introduction

Data constraints are used in structured and unstructured databases to capture real-world semantics observed in the modeled application domain. In our context, a constraint can be defined as a set of predicates P1 ∧ P2 ∧ ... P k . Each predicate is in the form C1 θC2, where C1 is an attribute, θ is a comparison operator and C2 is either an attribute or a constant [15]. Constraints are assertions on permissible or consistent database states, and specify certain properties of data that need to be satisfied by valid instances of the database.

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References

  1. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: VLDB Conference, Santiago, Cile (1994)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large data-bases. In: International Conference on Very Large Data Bases, pp. 478–499. Morgan Kaufmann, San Francisco (1994)

    Google Scholar 

  3. Apiletti, D., Baralis, E., Bruno, G., Ficarra, E.: Data Cleaning and Semantic Improvement in Biological Databases. Journal of Integrative Bio-informatics 3(2) (2006)

    Google Scholar 

  4. Atzeni, P., Ceri, S., Paraboschi, S., Torlone, R.: Database Systems - Concepts, Languages and Architectures. McGraw-Hill, New York (1999)

    Google Scholar 

  5. Baralis, E., Garza, P., Quintarelli, E., Tanca, L.: Answering Queries on XML Data by means of Association Rules. In: Lindner, W., Mesiti, M., Türker, C., Tzitzikas, Y., Vakali, A.I. (eds.) EDBT 2004. LNCS, vol. 3268, pp. 260–269. Springer, Heidelberg (2004)

    Google Scholar 

  6. Bodenreider, O., Aubry, M., Burgun, A.: Non-lexical approaches to iden-tifying Associative Relations in the Gene Ontology. In: Pacific Symposium on Biocomputing (2005)

    Google Scholar 

  7. Bruno, G., Garza, P., Quintarelli, E., Rossato, R.: Anomaly detection through quasi-functional dependency analysis. Special Issue of Advances in Querying Non-Conventional Data Sources, Journal of Digital Information Management (to appear)

    Google Scholar 

  8. Ceri, S., Di Giunta, F., Lanzi, P.L.: Mining Constraint Violations. ACM Transactions on Database Systems (TODS) 32(1) (2007)

    Google Scholar 

  9. Davidson, S.B., Kosky, A.S.: Wol: a language for database transforma-tions and constrains. In: Proceedings of the 13th IEEE International Conference on Data Engineering, pp. 55–65 (1997)

    Google Scholar 

  10. Fan, W., Simeon, J.: Integrity constraints for XML. In: ACM Symposium on Principles of Databases, PODS (2000)

    Google Scholar 

  11. Galhardas, H., Florescu, D., Shasha, D., Simon, E.: AJAX: An Extensible Data Cleaning Tool. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, Texas, United States, p. 590 (2000)

    Google Scholar 

  12. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)

    Google Scholar 

  13. Hara, C.S.: Reasoning about functional and key dependencies in hierar-chically structured data. Ph.D dissertation in Computer and Information Science, University of Pennsylvania (2004)

    Google Scholar 

  14. Hartmann, S., Link, S., Kirchberg, M.: A subgraph-based approach to-wards functional dependencies for XML. In: Proceedings of the 7th World-Multiconference on Systemics, Cybernetics and Informatics (SCI), Computer Science and Engineering II, Orlando, Florida, USA, July 27-30, vol. IX, pp. 200–205 (2003)

    Google Scholar 

  15. Isakbeyoglu, N.S., Ozsoyoglu, Z.M.: Maintenance of Implication Integrity Constraints under updates to Constraints. VLDB Journal 7, 67–78 (1998)

    Article  Google Scholar 

  16. Jeudy, B., Rioult, F.: Database Transposition for Constrained (Closed) Pattern Mining. In: Goethals, B., Siebes, A. (eds.) KDID 2004. LNCS, vol. 3377, pp. 89–107. Springer, Heidelberg (2005)

    Google Scholar 

  17. Koh, J.L.Y., Lee, M.L., Khan, A.M., Tan, P.T.J., Brusic, V.: Duplicate Detection in Biological Data using Association Rule Mining. In: 2nd European Workshop on Data Mining and Text Mining for Bioinformatics, ECML/PKDD workshop, Pisa, Italy, September 24 (2004)

    Google Scholar 

  18. Kumar, A., Smith, B., Borgelt, C.: Dependence Relationships between Gene Ontology Terms based on TIGR Gene Product Annotations. In: 3rd International Workshop on Computational Terminology, CompuTerm (2004)

    Google Scholar 

  19. Lee, M., Ling, T., Low, W.: Designing functional dependencies for XML. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 124–141. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  20. Lee, M.L., Ling, T.W., Low, W.L.: IntelliClean: A Knowledge-Based Intelligent Data Cleaner. In: KDD 2000, Boston (2000)

    Google Scholar 

  21. Müller, H., Naumann, F., Freytag, J.C.: Data quality in genome databases. In: Proceedings of the International Conference on Information Quality (IQ 2003), Boston (2003)

    Google Scholar 

  22. Saridakis, V., Christendat, D., Thygesen, A., Arrowsmith, C.H., Edwards, A.M., Pai, E.F.: Crystal structure of Methanobacterium thermoautotrophicum conserved protein MTH1020 reveals an NTN-hydrolase fold. Proteins 48(1), 141–143 (2002)

    Article  Google Scholar 

  23. Shahri, H.H., Barforush, A.A.: A Flexible Fuzzy Expert System for Fuzzy Duplicate Elimination in Data Cleaning. In: Galindo, F., Takizawa, M., Traunmüller, R. (eds.) DEXA 2004. LNCS, vol. 3180, pp. 161–170. Springer, Heidelberg (2004)

    Google Scholar 

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Apiletti, D., Bruno, G., Ficarra, E., Baralis, E. (2009). Extraction of Constraints from Biological Data. In: Sidhu, A.S., Dillon, T.S. (eds) Biomedical Data and Applications. Studies in Computational Intelligence, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02193-0_7

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  • DOI: https://doi.org/10.1007/978-3-642-02193-0_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02192-3

  • Online ISBN: 978-3-642-02193-0

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