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» Improving gene set analysis of microarray data by SAM-GS
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BMCBI
2011
12 years 11 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. ...
CSB
2005
IEEE
165views Bioinformatics» more  CSB 2005»
13 years 9 months ago
Sequential Diagonal Linear Discriminant Analysis (SeqDLDA) for Microarray Classification and Gene Identification
In microarray classification we are faced with a very large number of features and very few training samples. This is a challenge for classical Linear Discriminant Analysis (LDA),...
Roger Pique-Regi, Antonio Ortega, Shahab Asgharzad...
BMCBI
2005
124views more  BMCBI 2005»
13 years 7 months ago
ErmineJ: Tool for functional analysis of gene expression data sets
Background: It is common for the results of a microarray study to be analyzed in the context of biologically-motivated groups of genes such as pathways or Gene Ontology categories...
Homin K. Lee, William Braynen, Kiran Keshav, Paul ...
BIOINFORMATICS
2007
66views more  BIOINFORMATICS 2007»
13 years 7 months ago
Gene expression network analysis and applications to immunology
We address the problem of using expression data and prior biological knowledge to identify differentially expressed pathways or groups of genes. Following an idea of Ideker et al...
Serban Nacu, Rebecca Critchley-Thorne, Peter Lee, ...
BMCBI
2006
103views more  BMCBI 2006»
13 years 7 months ago
Improving missing value imputation of microarray data by using spot quality weights
Background: Microarray technology has become popular for gene expression profiling, and many analysis tools have been developed for data interpretation. Most of these tools requir...
Peter Johansson, Jari Häkkinen