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BMCBI
2006
170views more  BMCBI 2006»
13 years 8 months ago
Biclustering of gene expression data by non-smooth non-negative matrix factorization
Background: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of gene...
Pedro Carmona-Saez, Roberto D. Pascual-Marqui, Fra...
JCB
2000
103views more  JCB 2000»
13 years 7 months ago
Testing for Differentially-Expressed Genes by Maximum-Likelihood Analysis of Microarray Data
Although two-color uorescent DNA microarrays are now standard equipment in many molecular biology laboratories, methods for identifying differentially expressed genes in microarra...
Trey Ideker, Vesteinn Thorsson, Andrew F. Siegel, ...
SDM
2008
SIAM
123views Data Mining» more  SDM 2008»
13 years 9 months ago
Constrained Co-clustering of Gene Expression Data
In many applications, the expert interpretation of coclustering is easier than for mono-dimensional clustering. Co-clustering aims at computing a bi-partition that is a collection...
Ruggero G. Pensa, Jean-François Boulicaut
CBMS
2005
IEEE
14 years 1 months ago
An Ontology-Driven Clustering Method for Supporting Gene Expression Analysis
The Gene Ontology (GO) is an important knowledge resource for biologists and bioinformaticians. This paper explores the integration of similarity information derived from GO into ...
Haiying Wang, Francisco Azuaje, Olivier Bodenreide...
CSB
2004
IEEE
164views Bioinformatics» more  CSB 2004»
13 years 11 months ago
Biclustering in Gene Expression Data by Tendency
The advent of DNA microarray technologies has revolutionized the experimental study of gene expression. Clustering is the most popular approach of analyzing gene expression data a...
Jinze Liu, Jiong Yang, Wei Wang 0010