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» A Repulsive Clustering Algorithm for Gene Expression Data
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KDD
2003
ACM
190views Data Mining» more  KDD 2003»
14 years 8 months ago
Distance-enhanced association rules for gene expression
We introduce a novel data mining technique for the analysis of gene expression. Gene expression is the effective production of the protein that a gene encodes. We focus on the cha...
Aleksandar Icev, Carolina Ruiz, Elizabeth F. Ryder
BMCBI
2007
140views more  BMCBI 2007»
13 years 7 months ago
Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets
Background: Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles...
Michael Gormley, William Dampier, Adam Ertel, Bilg...
BMCBI
2007
182views more  BMCBI 2007»
13 years 7 months ago
Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data
Background: ChIP-chip data, which indicate binding of transcription factors (TFs) to DNA regions in vivo, are widely used to reconstruct transcriptional regulatory networks. Howev...
Wei-Sheng Wu, Wen-Hsiung Li, Bor-Sen Chen
CSB
2005
IEEE
146views Bioinformatics» more  CSB 2005»
14 years 1 months ago
Multi-Metric and Multi-Substructure Biclustering Analysis for Gene Expression Data
A good number of biclustering algorithms have been proposed for grouping gene expression data. Many of them have adopted matrix norms to define the similarity score of a bicluste...
Sun-Yuan Kung, Man-Wai Mak, Ilias Tagkopoulos
RECOMB
2006
Springer
14 years 8 months ago
Detecting MicroRNA Targets by Linking Sequence, MicroRNA and Gene Expression Data
MicroRNAs (miRNAs) have recently been discovered as an important class of non-coding RNA genes that play a major role in regulating gene expression, providing a means to control th...
Jim C. Huang, Quaid Morris, Brendan J. Frey