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Integrated analysis of gene expression by association rules discovery

Pedro Carmona-Saez1, Monica Chagoyen1, Andres Rodriguez2, Oswaldo Trelles2, Jose M Carazo1 and Alberto Pascual-Montano3*

Author affiliations

1 BioComputing Unit, National Center for Biotechnology (CNB-CSIC), Cantoblanco, 28049, Madrid, Spain

2 Computer Architecture Department, Universidad de Málaga, 29080, Málaga, Spain

3 Computer Architecture and System Engineering Department, Facultad de CC Físicas, Universidad Complutense de Madrid, 28040, Madrid, Spain

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Citation and License

BMC Bioinformatics 2006, 7:54  doi:10.1186/1471-2105-7-54

Published: 7 February 2006



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.


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 annotated with metabolic pathways, transcriptional regulators and Gene Ontology categories. Automatically extracted associations revealed significant relationships among these gene attributes and expression patterns, where many of them are clearly supported by recently reported work.


The integration of external biological information and gene expression data can provide insights about the biological processes associated to gene expression programs. In this paper we show that the proposed methodology is able to integrate multiple gene annotations and expression data in the same analytic framework and extract meaningful associations among heterogeneous sources of data. An implementation of the method is included in the Engene software package.