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BMCBI
2004
180views more  BMCBI 2004»
13 years 4 months ago
Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovaria
Background: A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identi...
Virginie M. Aris, Michael J. Cody, Jeff Cheng, Jam...
BMCBI
2010
135views more  BMCBI 2010»
13 years 4 months ago
Simple and flexible classification of gene expression microarrays via Swirls and Ripples
Background: A simple classification rule with few genes and parameters is desirable when applying a classification rule to new data. One popular simple classification rule, diagon...
Stuart G. Baker
JUCS
2008
139views more  JUCS 2008»
13 years 4 months ago
A Progressive Learning Method for Symbol Recognition
: This paper deals with a progressive learning method for symbol recognition which improves its own recognition rate when new symbols are recognized in graphic documents. We propos...
Sabine Barrat, Salvatore Tabbone
BMCBI
2006
147views more  BMCBI 2006»
13 years 4 months ago
Grouping Gene Ontology terms to improve the assessment of gene set enrichment in microarray data
Background: Gene Ontology (GO) terms are often used to assess the results of microarray experiments. The most common way to do this is to perform Fisher's exact tests to find...
Alex Lewin, Ian C. Grieve
BMCBI
2005
124views more  BMCBI 2005»
13 years 4 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 ...