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» A stable gene selection in microarray data analysis
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BMCBI
2004
115views more  BMCBI 2004»
13 years 7 months ago
Quantifying the relationship between co-expression, co-regulation and gene function
Background: It is thought that genes with similar patterns of mRNA expression and genes with similar functions are likely to be regulated via the same mechanisms. It has been diff...
Dominic J. Allocco, Isaac S. Kohane, Atul J. Butte
BMCBI
2007
159views more  BMCBI 2007»
13 years 8 months ago
Detecting differential expression in microarray data: comparison of optimal procedures
Background: Many procedures for finding differentially expressed genes in microarray data are based on classical or modified t-statistics. Due to multiple testing considerations, ...
Elena Perelman, Alexander Ploner, Stefano Calza, Y...
IDEAL
2007
Springer
14 years 2 months ago
Analysis of Tiling Microarray Data by Learning Vector Quantization and Relevance Learning
We apply learning vector quantization to the analysis of tiling microarray data. As an example we consider the classification of C. elegans genomic probes as intronic or exonic. T...
Michael Biehl, Rainer Breitling, Yang Li
BIOINFORMATICS
2007
190views more  BIOINFORMATICS 2007»
13 years 7 months ago
Towards clustering of incomplete microarray data without the use of imputation
Motivation: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by...
Dae-Won Kim, Ki Young Lee, Kwang H. Lee, Doheon Le...
BMCBI
2010
124views more  BMCBI 2010»
13 years 8 months ago
A factor model to analyze heterogeneity in gene expression
Background: Microarray technology allows the simultaneous analysis of thousands of genes within a single experiment. Significance analyses of transcriptomic data ignore the gene d...
Yuna Blum, Guillaume Le Mignon, Sandrine Lagarrigu...