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A power law global error model for the identification of differentially expressed genes in microarray data



A power law global error model for the identification of differentially expressed genes in microarray data



Bmc Bioinformatics 5: 203-203



Background: High-density oligonucleotide microarray technology enables the discovery of genes that are transcriptionally modulated in different biological samples due to physiology, disease or intervention. Methods for the identification of these so-called "differentially expressed genes" (DEG) would largely benefit from a deeper knowledge of the intrinsic measurement variability.

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Accession: 011702293

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PMID: 15606915

DOI: 10.1186/1471-2105-5-203


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