Skip to Main content Skip to Navigation
Journal articles

Mining association rule bases from integrated genomic data and annotations (extended version)

Abstract : During the last decade, several clustering and association rule mining techniques have been applied to highlight groups of co-regulated genes in gene expression data. Nowadays, integrating these data and biological knowledge into a single framework has become a ma- jor challenge to improve the relevance of mined patterns and simplify their interpretation by biologists. GenMiner was developed for mining association rules from such integrated datasets. It combines a new normalized discretization method, called NorDi, and the JClose algorithm to extract condensed representations for association rules. Experimental results show that GenMiner requires less memory than Apriori based approaches and that it improves the relevance of extracted rules. Moreover, association rules obtained revealed significant co-annotated and co-expressed gene patterns showing important biological relationships supported by recent biological literature.
Complete list of metadatas

Cited literature [22 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00361770
Contributor : Nicolas Pasquier <>
Submitted on : Sunday, April 25, 2010 - 2:41:02 PM
Last modification on : Tuesday, May 26, 2020 - 6:50:36 PM
Document(s) archivé(s) le : Tuesday, September 14, 2010 - 5:00:30 PM

File

Martinez_Pasquier_Pasquier_-_2...
Publisher files allowed on an open archive

Identifiers

  • HAL Id : hal-00361770, version 1

Collections

Citation

Ricardo Martinez, Nicolas Pasquier, Claude Pasquier. Mining association rule bases from integrated genomic data and annotations (extended version). Lecture Notes in Bioinformatics, LNCS 5488, 2009, pp. . ⟨hal-00361770⟩

Share

Metrics

Record views

268

Files downloads

128