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» Evaluation of clustering algorithms for gene expression data
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BMCBI
2007
134views more  BMCBI 2007»
14 years 9 months ago
Nearest Neighbor Networks: clustering expression data based on gene neighborhoods
Background: The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both indiv...
Curtis Huttenhower, Avi I. Flamholz, Jessica N. La...
BMCBI
2007
112views more  BMCBI 2007»
14 years 9 months ago
Inferring biological functions and associated transcriptional regulators using gene set expression coherence analysis
Background: Gene clustering has been widely used to group genes with similar expression pattern in microarray data analysis. Subsequent enrichment analysis using predefined gene s...
Tae-Min Kim, Yeun-Jun Chung, Mun-Gan Rhyu, Myeong ...
CSB
2004
IEEE
136views Bioinformatics» more  CSB 2004»
15 years 1 months ago
Minimum Entropy Clustering and Applications to Gene Expression Analysis
Clustering is a common methodology for analyzing the gene expression data. In this paper, we present a new clustering algorithm from an information-theoretic point of view. First,...
Haifeng Li, Keshu Zhang, Tao Jiang
RECOMB
2001
Springer
15 years 9 months ago
Context-specific Bayesian clustering for gene expression data
The recent growth in genomic data and measurements of genome-wide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors. I...
Yoseph Barash, Nir Friedman
SDM
2004
SIAM
187views Data Mining» more  SDM 2004»
14 years 10 months ago
Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data
Microarray experiments have been extensively used for simultaneously measuring DNA expression levels of thousands of genes in genome research. A key step in the analysis of gene e...
Hyuk Cho, Inderjit S. Dhillon, Yuqiang Guan, Suvri...