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» A stable gene selection in microarray data analysis
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BMCBI
2004
150views more  BMCBI 2004»
13 years 7 months ago
Graph-based iterative Group Analysis enhances microarray interpretation
Background: One of the most time-consuming tasks after performing a gene expression experiment is the biological interpretation of the results by identifying physiologically impor...
Rainer Breitling, Anna Amtmann, Pawel Herzyk
NIPS
2003
13 years 9 months ago
ICA-based Clustering of Genes from Microarray Expression Data
We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model of genomic expression pa...
Su-In Lee, Serafim Batzoglou
BMCBI
2007
185views more  BMCBI 2007»
13 years 7 months ago
GEDI: a user-friendly toolbox for analysis of large-scale gene expression data
Background: Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, a...
André Fujita, João Ricardo Sato, Car...
BMCBI
2008
169views more  BMCBI 2008»
13 years 7 months ago
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
Background: Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular sig...
Alexander R. Statnikov, Lily Wang, Constantin F. A...
BMCBI
2008
121views more  BMCBI 2008»
13 years 7 months ago
Microarray data mining using landmark gene-guided clustering
Background: Clustering is a popular data exploration technique widely used in microarray data analysis. Most conventional clustering algorithms, however, generate only one set of ...
Pankaj Chopra, Jaewoo Kang, Jiong Yang, HyungJun C...