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Bioinformatics Advance Access originally published online on September 17, 2008
Bioinformatics 2008 24(22):2643-2644; doi:10.1093/bioinformatics/btn490
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

GenMiner: mining non-redundant association rules from integrated gene expression data and annotations

Ricardo Martinez 1, Nicolas Pasquier 1,* and Claude Pasquier 2

1Laboratoire I3S, UNSA/CNRS UMR-6070, 2000 route des Lucioles, 06903 Valbonne and 2IDBC, UNSA/CNRS UMR-6543, Parc Valrose, 06108 Nice, France

*To whom correspondence should be addressed.


  Abstract

Summary: GenMiner is an implementation of association rule discovery dedicated to the analysis of genomic data. It allows the analysis of datasets integrating multiple sources of biological data represented as both discrete values, such as gene annotations, and continuous values, such as gene expression measures. GenMiner implements the new NorDi (normal discretization) algorithm for normalizing and discretizing continuous values and takes advantage of the Close algorithm to efficiently generate minimal non-redundant association rules. Experiments show that execution time and memory usage of GenMiner are significantly smaller than those of the standard Apriori-based approach, as well as the number of extracted association rules.

Availability: The GenMiner software and supplementary materials are available at http://bioinfo.unice.fr/publications/genminer_article/ and http://keia.i3s.unice.fr/?Implementations:GenMiner

Contact: pasquier{at}unice.fr

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Jonathan Wren

Received on May 20, 2008; revised on August 18, 2008; accepted on September 14, 2008

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