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» Evaluation of clustering algorithms for gene expression data
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BMCBI
2010
136views more  BMCBI 2010»
15 years 6 months ago
The IronChip evaluation package: a package of perl modules for robust analysis of custom microarrays
Background: Gene expression studies greatly contribute to our understanding of complex relationships in gene regulatory networks. However, the complexity of array design, producti...
Yevhen Vainshtein, Mayka Sanchez, Alvis Brazma, Ma...
BMCBI
2006
153views more  BMCBI 2006»
15 years 6 months ago
Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments
Background: The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating v...
Maureen A. Sartor, Craig R. Tomlinson, Scott C. We...
JCB
2008
170views more  JCB 2008»
15 years 6 months ago
Efficiently Identifying Max-Gap Clusters in Pairwise Genome Comparison
The spatial clustering of genes across different genomes has been used to study important problems in comparative genomics, from identification of operons to detection of homologo...
Xu Ling, Xin He, Dong Xin, Jiawei Han
BMCBI
2007
139views more  BMCBI 2007»
15 years 6 months ago
Significance analysis of microarray transcript levels in time series experiments
Background: Microarray time series studies are essential to understand the dynamics of molecular events. In order to limit the analysis to those genes that change expression over ...
Barbara Di Camillo, Gianna Toffolo, Sreekumaran K....
CSB
2005
IEEE
143views Bioinformatics» more  CSB 2005»
15 years 12 months ago
Multivariate gene selection: Does it help
When building predictors of disease state based on gene expression data, gene selection is performed in order to achieve a good performance and to identify a relevant subset of ge...
Carmen Lai, Marcel J. T. Reinders