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» Improving gene set analysis of microarray data by SAM-GS
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BMCBI
2005
131views more  BMCBI 2005»
13 years 7 months ago
Regularized Least Squares Cancer Classifiers from DNA microarray data
Background: The advent of the technology of DNA microarrays constitutes an epochal change in the classification and discovery of different types of cancer because the information ...
Nicola Ancona, Rosalia Maglietta, Annarita D'Addab...
BMCBI
2008
133views more  BMCBI 2008»
13 years 7 months ago
CORE_TF: a user-friendly interface to identify evolutionary conserved transcription factor binding sites in sets of co-regulated
Background: The identification of transcription factor binding sites is difficult since they are only a small number of nucleotides in size, resulting in large numbers of false po...
Matthew S. Hestand, Michiel van Galen, Michel P. V...
BMCBI
2008
162views more  BMCBI 2008»
13 years 7 months ago
Background correction using dinucleotide affinities improves the performance of GCRMA
Background: High-density short oligonucleotide microarrays are a primary research tool for assessing global gene expression. Background noise on microarrays comprises a significan...
Raad Z. Gharaibeh, Anthony Fodor, Cynthia Gibas
JCB
2002
160views more  JCB 2002»
13 years 7 months ago
Inference from Clustering with Application to Gene-Expression Microarrays
There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different u...
Edward R. Dougherty, Junior Barrera, Marcel Brun, ...
BMCBI
2004
124views more  BMCBI 2004»
13 years 7 months ago
Tests for finding complex patterns of differential expression in cancers: towards individualized medicine
Background: Microarray studies in cancer compare expression levels between two or more sample groups on thousands of genes. Data analysis follows a population-level approach (e.g....
James Lyons-Weiler, Satish Patel, Michael J. Becic...