Bio-medical Ontologies Maintenance and Change Management

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.
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