Detecting inconsistency in biological molecular databases using ontologies

Authors:
Qingfeng Chen;Yi-Ping Phoebe Chen;Chengqi Zhang
Affiliations:
School of Information Technology, Deakin University, Melbourne, Australia 3125;School of Information Technology, Deakin University, Melbourne, Australia 3125 and ARC Centre in Bioinformatics, Melbourne, Australia;Faculty of Information Technology, University of Technology, Sydney, Australia 2007
Venue:
Data Mining and Knowledge Discovery
Year:
2007

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Abstract

The rapid growth of life science databases demands the fusion of knowledge from heterogeneous databases to answer complex biological questions. The discrepancies in nomenclature, various schemas and incompatible formats of biological databases, however, result in a significant lack of interoperability among databases. Therefore, data preparation is a key prerequisite for biological database mining. Integrating diverse biological molecular databases is an essential action to cope with the heterogeneity of biological databases and guarantee efficient data mining. However, the inconsistency in biological databases is a key issue for data integration. This paper proposes a framework to detect the inconsistency in biological databases using ontologies. A numeric estimate is provided to measure the inconsistency and identify those biological databases that are appropriate for further mining applications. This aids in enhancing the quality of databases and guaranteeing accurate and efficient mining of biological databases.