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Journal of Biomedicine and Biotechnology
Volume 2005 (2005), Issue 2, Pages 181-188
Research article

Online Analytical Processing (OLAP): A Fast and Effective Data Mining Tool for Gene Expression Databases

1Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville 20705, MD, USA
2School of Computational Sciences, George Mason University, Fairfax 22030, VA, USA

Received 27 July 2004; Revised 26 November 2004; Accepted 7 December 2004

Copyright © 2005 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Gene expression databases contain a wealth of information, but current data mining tools are limited in their speed and effectiveness in extracting meaningful biological knowledge from them. Online analytical processing (OLAP) can be used as a supplement to cluster analysis for fast and effective data mining of gene expression databases. We used Analysis Services 2000, a product that ships with SQLServer2000, to construct an OLAP cube that was used to mine a time series experiment designed to identify genes associated with resistance of soybean to the soybean cyst nematode, a devastating pest of soybean. The data for these experiments is stored in the soybean genomics and microarray database (SGMD). A number of candidate resistance genes and pathways were found. Compared to traditional cluster analysis of gene expression data, OLAP was more effective and faster in finding biologically meaningful information. OLAP is available from a number of vendors and can work with any relational database management system through OLE DB.