Integrated analysis of gene expression by association rules discovery

Background: Microarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditions. To extract biological knowledge, and to fully understand such datasets, it is essential to include external biological information about genes and gene products to the analysis of expression data. However, most of the current approaches to analyze microarray datasets are mainly focused on the analysis of experimental data, and external biological information is incorporated as a posterior process. Results: In this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique. The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns. We applied the proposed methodology to the analysis of gene expression datasets in which genes were annota...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Pedro Carmona-Saez, Monica Chagoyen, Andrés Rodríguez, Oswaldo Trelles, José María Carazo, Alberto D. Pascual-Montano
• Keywords: BMCBI 2006, Expression Data, External Biological Information, Gene