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» Combined Gene Selection Methods for Microarray Data Analysis
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BMCBI
2007
117views more  BMCBI 2007»
15 years 2 months ago
Meta-analysis of several gene lists for distinct types of cancer: A simple way to reveal common prognostic markers
Background: Although prognostic biomarkers specific for particular cancers have been discovered, microarray analysis of gene expression profiles, supported by integrative analysis...
Xinan Yang, Xiao Sun
BMCBI
2004
132views more  BMCBI 2004»
15 years 2 months ago
Two-stage normalization using background intensities in cDNA microarray data
Background: In the microarray experiment, many undesirable systematic variations are commonly observed. Normalization is the process of removing such variation that affects the me...
Dankyu Yoon, Sung-Gon Yi, Ju-Han Kim, Taesung Park
BMCBI
2004
139views more  BMCBI 2004»
15 years 2 months ago
Resolution of large and small differences in gene expression using models for the Bayesian analysis of gene expression levels an
Background: The detection of small yet statistically significant differences in gene expression in spotted DNA microarray studies is an ongoing challenge. Meeting this challenge r...
Jeffrey P. Townsend
PRL
2006
130views more  PRL 2006»
15 years 2 months ago
Efficient huge-scale feature selection with speciated genetic algorithm
With increasing interest in bioinformatics, sophisticated tools are required to efficiently analyze gene information. The classification of gene expression profiles is crucial in ...
Jin-Hyuk Hong, Sung-Bae Cho
CIBCB
2005
IEEE
15 years 8 months ago
Two-Phase EA/k-NN for Feature Selection and Classification in Cancer Microarray Datasets
Efficient and reliable methods that can find a small sample of informative genes amongst thousands are of great importance. In this area, much research is investigating the combina...
Thorhildur Juliusdottir, David Corne, Ed Keedwell,...