Journal Article

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

Ricardo Martinez, Nicolas Pasquier and Claude Pasquier

in Bioinformatics

Volume 24, issue 22, pages 2643-2644
Published in print November 2008 | ISSN: 1367-4803
Published online September 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn490
GenMiner: mining non-redundant association rules from integrated gene expression data and annotations

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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@unice.fr

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  1461 words. 

Subjects: Bioinformatics and Computational Biology

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