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» Combined Gene Selection Methods for Microarray Data Analysis
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BMCBI
2010
111views more  BMCBI 2010»
15 years 3 months ago
Functional Analysis: Evaluation of Response Intensities - Tailoring ANOVA for Lists of Expression Subsets
Background: Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions....
Fabrice Berger, Bertrand De Meulder, Anthoula Gaig...
PAKDD
2005
ACM
114views Data Mining» more  PAKDD 2005»
15 years 8 months ago
Increasing Classification Accuracy by Combining Adaptive Sampling and Convex Pseudo-Data
The availability of microarray data has enabled several studies on the application of aggregated classifiers for molecular classification. We present a combination of classifier ag...
Chia Huey Ooi, Madhu Chetty
JBI
2004
171views Bioinformatics» more  JBI 2004»
15 years 4 months ago
Consensus Clustering and Functional Interpretation of Gene Expression Data
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene-expression profiles to clusters. Obtaining a consensus s...
Paul Kellam, Stephen Swift, Allan Tucker, Veronica...
BMCBI
2006
143views more  BMCBI 2006»
15 years 3 months ago
Discovering functional gene expression patterns in the metabolic network of Escherichia coli with wavelets transforms
Background: Microarray technology produces gene expression data on a genomic scale for an endless variety of organisms and conditions. However, this vast amount of information nee...
Rainer König, Gunnar Schramm, Marcus Oswald, ...
BMCBI
2006
190views more  BMCBI 2006»
15 years 3 months ago
SNPs3D: Candidate gene and SNP selection for association studies
Background: The relationship between disease susceptibility and genetic variation is complex, and many different types of data are relevant. We describe a web resource and databas...
Peng Yue, Eugene Melamud, John Moult