Data Cleaning and Semantic Improvement in Biological Databases

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doi doi:10.2390/biecoll-jib-2006-40
submission July 24, 2006
published August 28, 2006

Daniele Apiletti, Giulia Bruno, Elisa Ficarra and Elena Baralis

Correspondence should be addressed to:
Elisa Ficarra
DAUIN - Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy
ti.otilop@nullarracif.asile


Abstract

Public genomic and proteomic databases can be affected by a variety of errors. These errors may involve either the description or the meaning of data (namely, syntactic or semantic errors). We focus our analysis on the detection of semantic errors, in order to verify the accuracy of the stored information. In particular, we address the issue of data constraints and functional dependencies among attributes in a given relational database. Constraints and dependencies show semantics among attributes in a database schema and their knowledge may be exploited to improve data quality and integration in database design, and to perform query optimization and dimensional reduction. We propose a method to discover data constraints and functional dependencies by means of association rule mining. Association rules are extracted among attribute values and allows us to find causality relationships among them. Then, by analyzing the support and confidence of each rule, (probabilistic) data constraints and functional dependencies may be detected. With our method we can both show the presence of erroneous data and highlight novel semantic information. Moreover, our method is database-independent because it infers rules from data. In this paper, we report the application of our techniques to the SCOP (Structural Classification of Proteins) and CATH Protein Structure Classification databases.

Reference

D. Apiletti, G. Bruno, E. Ficarra and E. Baralis. Data Cleaning and Semantic Improvement in Biological Databases. Journal of Integrative Bioinformatics, 3(2):40, 2006. Online Journal: http://journal.imbio.de/index.php?paper_id=40
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