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GenMiner: mining informative association rules from genomic data

Abstract : GENMINER is a smart adaptation of closed itemsets based association rules extraction to genomic data. It takes advantage of the novel NORDI discretization method and of the CLOSE algorithm to efficiently generate minimal non-redundant association rules. GENMINER facilitates the integration of numerous sources of biological information such as gene expressions and annotations, and can tacitly integrate qualitative information on biological conditions (age, sex, etc.). We validated this approach analyzing the microarray datasets used by Eisen et al. with several sources of biological annotations. Extracted associations revealed significantco-annotatedand co-expressed gene patterns, showing important biological relationships between genes and their features. Several of these relationships are supported by recent biological literature.
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Contributor : Claude Pasquier <>
Submitted on : Monday, May 25, 2015 - 8:08:04 AM
Last modification on : Tuesday, May 26, 2020 - 6:50:36 PM


  • HAL Id : hal-01154856, version 1



Ricardo Martinez, Claude Pasquier, Nicolas Pasquier. GenMiner: mining informative association rules from genomic data. IEEE International Conference on Bioinformatics and Biomedicine (BIBM'07), Nov 2007, Silicon Valley, United States. ⟨hal-01154856⟩



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