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» Combining microarrays and genetic analysis
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BMCBI
2010
214views more  BMCBI 2010»
13 years 10 months ago
AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number
Background: Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the under...
Aaron M. Newman, James B. Cooper
GPB
2010
231views Solid Modeling» more  GPB 2010»
13 years 7 months ago
Mining Gene Expression Profiles: An Integrated Implementation of Kernel Principal Component Analysis and Singular Value Decompos
The detection of genes that show similar profiles under different experimental conditions is often an initial step in inferring the biological significance of such genes. Visualiz...
Ferran Reverter, Esteban Vegas, Pedro Sánch...
BMCBI
2007
149views more  BMCBI 2007»
13 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
2006
97views more  BMCBI 2006»
13 years 10 months ago
Goulphar: rapid access and expertise for standard two-color microarray normalization methods
Background: Raw data normalization is a critical step in microarray data analysis because it directly affects data interpretation. Most of the normalization methods currently used...
Sophie Lemoine, Florence Combes, Nicolas Servant, ...
BMCBI
2006
126views more  BMCBI 2006»
13 years 10 months ago
OpWise: Operons aid the identification of differentially expressed genes in bacterial microarray experiments
Background: Differentially expressed genes are typically identified by analyzing the variation between replicate measurements. These procedures implicitly assume that there are no...
Morgan N. Price, Adam P. Arkin, Eric J. Alm