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BMCBI
2010
124views more  BMCBI 2010»
14 years 10 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...
BMCBI
2006
123views more  BMCBI 2006»
14 years 9 months ago
How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different result
Background: Short oligonucleotide arrays for transcript profiling have been available for several years. Generally, raw data from these arrays are analysed with the aid of the Mic...
Frank F. Millenaar, John Okyere, Sean T. May, Mart...
KDD
2002
ACM
145views Data Mining» more  KDD 2002»
15 years 10 months ago
Handling very large numbers of association rules in the analysis of microarray data
The problem of analyzing microarray data became one of important topics in bioinformatics over the past several years, and different data mining techniques have been proposed for ...
Alexander Tuzhilin, Gediminas Adomavicius
BMCBI
2007
149views more  BMCBI 2007»
14 years 10 months ago
A unified framework for finding differentially expressed genes from microarray experiments
Background: This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modul...
Jahangheer S. Shaik, Mohammed Yeasin
BMCBI
2004
106views more  BMCBI 2004»
14 years 9 months ago
Spotting effect in microarray experiments
Background: Microarray data must be normalized because they suffer from multiple biases. We have identified a source of spatial experimental variability that significantly affects...
Tristan Mary-Huard, Jean-Jacques Daudin, Sté...