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BMCBI
2010
139views more  BMCBI 2010»
13 years 7 months ago
A highly efficient multi-core algorithm for clustering extremely large datasets
Background: In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput t...
Johann M. Kraus, Hans A. Kestler
BMCBI
2006
144views more  BMCBI 2006»
13 years 7 months ago
More robust detection of motifs in coexpressed genes by using phylogenetic information
Background: Several motif detection algorithms have been developed to discover overrepresented motifs in sets of coexpressed genes. However, in a noisy gene list, the number of ge...
Pieter Monsieurs, Gert Thijs, Abeer A. Fadda, Sigr...
ICML
2004
IEEE
14 years 8 months ago
K-means clustering via principal component analysis
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Chris H. Q. Ding, Xiaofeng He
BMCBI
2007
120views more  BMCBI 2007»
13 years 7 months ago
Transcript-based redefinition of grouped oligonucleotide probe sets using AceView: High-resolution annotation for microarrays
Background: Extracting biological information from high-density Affymetrix arrays is a multi-step process that begins with the accurate annotation of microarray probes. Shortfalls...
Jun Lu, Joseph C. Lee, Marc L. Salit, Margaret C. ...
BMCBI
2004
106views more  BMCBI 2004»
13 years 7 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...