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» Clustering of Gene Expression Data: Performance and Similari...
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BMCBI
2010
153views more  BMCBI 2010»
14 years 8 months ago
DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules
Background: Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differen...
Bruno M. Tesson, Rainer Breitling, Ritsert C. Jans...
BMCBI
2010
214views more  BMCBI 2010»
14 years 9 months ago
AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number
Background: Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the under...
Aaron M. Newman, James B. Cooper
92
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BMCBI
2008
146views more  BMCBI 2008»
14 years 9 months ago
A phase synchronization clustering algorithm for identifying interesting groups of genes from cell cycle expression data
Background: The previous studies of genome-wide expression patterns show that a certain percentage of genes are cell cycle regulated. The expression data has been analyzed in a nu...
Chang Sik Kim, Cheol Soo Bae, Hong Joon Tcha
BMCBI
2011
14 years 4 months ago
Multivariate analysis of microarray data: differential expression and differential connection
Background: Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically all...
Harri T. Kiiveri
BMCBI
2011
14 years 1 months ago
Gene set analysis for longitudinal gene expression data
Background: Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations. ...
Ke Zhang, Haiyan Wang, Arne C. Bathke, Solomon W. ...