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» Classification of microarray data using gene networks
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120
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BMCBI
2008
144views more  BMCBI 2008»
15 years 2 months ago
WGCNA: an R package for weighted correlation network analysis
Background: Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method ...
Peter Langfelder, Steve Horvath
140
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SDM
2008
SIAM
157views Data Mining» more  SDM 2008»
15 years 4 months ago
ROC-tree: A Novel Decision Tree Induction Algorithm Based on Receiver Operating Characteristics to Classify Gene Expression Data
Gene expression information from microarray experiments is a primary form of data for biological analysis and can offer insights into disease processes and cellular behaviour. Suc...
M. Maruf Hossain, Md. Rafiul Hassan, James Bailey
BMCBI
2005
110views more  BMCBI 2005»
15 years 2 months ago
Considerations when using the significance analysis of microarrays (SAM) algorithm
Background: Users of microarray technology typically strive to use universally acceptable data analysis strategies to determine significant expression changes in their experiments...
Ola Larsson, Claes Wahlestedt, James A. Timmons
BMCBI
2005
132views more  BMCBI 2005»
15 years 2 months ago
Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein
Background: A large number of papers have been published on analysis of microarray data with particular emphasis on normalization of data, detection of differentially expressed ge...
Gajendra P. S. Raghava, Joon H. Han
BMCBI
2006
155views more  BMCBI 2006»
15 years 2 months ago
Analysis of promoter regions of co-expressed genes identified by microarray analysis
Background: The use of global gene expression profiling to identify sets of genes with similar expression patterns is rapidly becoming a widespread approach for understanding biol...
Srinivas Veerla, Mattias Höglund