Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data
-
* Corresponding author: Olivier Gandrillon Gandrillon@maccgmc.univ-lyon1.fr
Genome Biology 2002, 3:research0067-research0067.16 doi:10.1186/gb-2002-3-12-research0067
Accesses
- Last 30 days: 99 accesses
- Last 365 days: 1060 accesses
- All time: 13014 accesses
Cited by
BioMed Central: 6 citations
|
Introduction
Discovery of error-tolerant biclusters from noisy gene expression data Rohit Gupta, Navneet Rao, Vipin Kumar BMC Bioinformatics 2011, 12(Suppl 12):S1 (24 November 2011) |
|
Proceedings
Identification of temporal association rules from time-series microarray data sets Hojung Nam, KiYoung Lee, Doheon Lee BMC Bioinformatics 2009, 10(Suppl 3):S6 (19 March 2009) |
|
Database
SQUAT: A web tool to mine human, murine and avian SAGE data Johan Leyritz, Stéphane Schicklin, Sylvain Blachon, Céline Keime, Céline Robardet, Jean-François Boulicaut, Jérémy Besson, Ruggero G Pensa, Olivier Gandrillon BMC Bioinformatics 2008, 9:378 (18 September 2008) |
|
Review
Clustering-based approaches to SAGE data mining Haiying Wang, Huiru Zheng, Francisco Azuaje BioData Mining 2008, 1:5 (17 July 2008) |
|
Research article
Large-scale analysis by SAGE reveals new mechanisms of v-erbA oncogene action Corinne Bresson, Celine Keime, Claudine Faure, Yann Letrillard, Maud Barbado, Sandra Sanfilippo, Najate Benhra, Olivier Gandrillon, Sandrine Gonin-Giraud BMC Genomics 2007, 8:390 (26 October 2007) |
|
Research article
Haiying Wang, Huiru Zheng, David Simpson, Francisco Azuaje BMC Bioinformatics 2006, 7:116 (8 March 2006) |