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» Evaluation of clustering algorithms for gene expression data
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BIBE
2007
IEEE
195views Bioinformatics» more  BIBE 2007»
15 years 3 months ago
Finding Clusters of Positive and Negative Coregulated Genes in Gene Expression Data
— In this paper, we propose a system for finding partial positive and negative coregulated gene clusters in microarray data. Genes are clustered together if they show the same p...
Kerstin Koch, Stefan Schönauer, Ivy Jansen, J...
BMCBI
2008
117views more  BMCBI 2008»
14 years 9 months ago
New resampling method for evaluating stability of clusters
Background: Hierarchical clustering is a widely applied tool in the analysis of microarray gene expression data. The assessment of cluster stability is a major challenge in cluste...
Irina Gana Dresen, Tanja Boes, Johannes Hüsin...
CSDA
2008
128views more  CSDA 2008»
14 years 9 months ago
Assessing agreement of clustering methods with gene expression microarray data
In the rapidly evolving field of genomics, many clustering and classification methods have been developed and employed to explore patterns in gene expression data. Biologists face...
Xueli Liu, Sheng-Chien Lee, George Casella, Gary F...
BMCBI
2006
155views more  BMCBI 2006»
14 years 9 months ago
AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data
Background: DNA microarrays are a powerful tool for monitoring the expression of tens of thousands of genes simultaneously. With the advance of microarray technology, the challeng...
Guoqing Lu, The V. Nguyen, Yuannan Xia, Michael Fr...
BMCBI
2005
122views more  BMCBI 2005»
14 years 9 months ago
GenClust: A genetic algorithm for clustering gene expression data
Background: Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designe...
Vito Di Gesù, Raffaele Giancarlo, Giosu&egr...