Bio-medical Ontologies Maintenance and Change Management

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184 D. Apiletti et al. Table 4. Functional dependencies among attributes in the SCOP database attributes original faulted cf cl 1.000000 0.999774 dm cf 1.000000 0.999972 dm cl 1.000000 1.000000 dm fa 1.000000 0.999944 dm sf 1.000000 0.999945 fa cf 1.000000 0.999972 fa cl 1.000000 1.000000 fa sf 1.000000 0.999944 px cf 1.000000 1.000000 px cl 1.000000 1.000000 px dm 1.000000 1.000000 px fa 1.000000 1.000000 px sf 1.000000 1.000000 px sp 1.000000 1.000000 sf cf 1.000000 0.999916 sf cl 1.000000 1.000000 sp cf 1.000000 0.999972 sp cl 1.000000 1.000000 sp dm 1.000000 0.999817 sp fa 1.000000 0.999972 sp sf 1.000000 0.999945 For example, in the SCOP database, the record of the protein chain px=100068 consists of the following attributes: • class cl=46456 (alpha proteins), • fold cf=46457 (globin-like), • super family sf=46458 (globin-like again), • family fa=46459 (truncated hemoglobin), • protein domain dm=46460 (hemoglobin), • species sp=46461 (ciliate). One of the injected faults is the misclassification of the record by assigning the value fa=46463 (globins) instead of fa=46459 (truncated hemoglobin) to the family attribute. The faulty value fa=46463 (globins) actually exists in the database and is the correct family attribute of many other proteins. In this example, the rule protein domain dm=46640 → family fa=46459 occurs in 26 records with confidence=0.963. We classified records that do not respect such rule as anomalies. Thus, the records with protein domain dm=46640 and
Extraction of Constraints from Biological Data 185 family fa≠46459 are selected as either candidate inconsistency or information for further investigation by biological experts. 6 Conclusions In this chapter we present a framework for the application of data mining tools to constraint extraction in the biological domain. We focus on tuple constraint and functional dependency detection in representative biological databases by means of association rule mining. By analyzing association rules we can deduce not only constraints and dependencies, which provide structural knowledge on a dataset and may be useful to perform query optimization or dimensional reduction, but also anomalies in data, which could be errors or interesting exceptions to be highlighted to domain experts. We have applied our analysis to the SCOP and CATH databases. We plan to extend our approach to different database models, such as XML or collections of relational tables, and to integrate automatic distributed inquiry about the detected anomalies on other databases, in order to help domain experts to distinguish biological anomalies from errors. Further developments of this work include the application of our method to heterogeneous data sources, to derive schema information that may be exploited during data integration. Acknowledgements We would like to thank Paolo Garza for his help in association rule extraction and for many stimulating discussions. References [1] Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: VLDB Conference, Santiago, Cile (1994) [2] Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: International Conference on Very Large Data Bases, pp. 478–499. Morgan Kaufmann, San Francisco (1994) [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) [4] Atzeni, P., Ceri, S., Paraboschi, S., Torlone, R.: Database Systems - Concepts, Languages and Architectures. McGraw-Hill, New York (1999) [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) [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)
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Amandeep S. Sidhu and Tharam S. Dil
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Amandeep S. Sidhu and Tharam S. Dil
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Preface The molecular biology commu
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Preface VII biomedical systems, and
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X Contents Mining Clinical, Immunol
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2 A.S. Sidhu, M. Bellgard, and T.S.
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Towards Bioinformatics Resourceomes
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2 The New Waves Towards Bioinformat
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2.4 Semantic Web Towards Bioinforma
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A Summary of Genomic Databases: Ove
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Appendix A Summary of Genomic Datab
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Protein Data Integration Problem Am
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Protein Data Integration Problem 57
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Fig. 3. Class Hierarchy of Protein
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Mining Clinical, Immunological, and
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Substructure Analysis of Metabolic
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entry compound relation subtype com
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Running Time 4500 4000 3500 3000 25
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6 Conclusion Substructure Analysis
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Completing the Total Wellbeing Puzz
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Genetic Algorithm in Ab Initio Prot
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Author Index Apiletti, Daniele 169
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Bio-medical Ontologies Maintenance and Change Management

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