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JIPS
2007
134views more  JIPS 2007»
13 years 5 months ago
An Efficient Functional Analysis Method for Micro-array Data Using Gene Ontology
: Microarray data includes tens of thousands of gene expressions simultaneously, so it can be effectively used in identifying the phenotypes of diseases. However, the retrieval of ...
Dong-wan Hong, Jong-keun Lee, Sung-soo Park, Sang-...
KDD
2004
ACM
302views Data Mining» more  KDD 2004»
14 years 6 months ago
Redundancy based feature selection for microarray data
In gene expression microarray data analysis, selecting a small number of discriminative genes from thousands of genes is an important problem for accurate classification of diseas...
Lei Yu, Huan Liu
BMCBI
2004
132views more  BMCBI 2004»
13 years 5 months ago
Two-stage normalization using background intensities in cDNA microarray data
Background: In the microarray experiment, many undesirable systematic variations are commonly observed. Normalization is the process of removing such variation that affects the me...
Dankyu Yoon, Sung-Gon Yi, Ju-Han Kim, Taesung Park
BMCBI
2004
106views more  BMCBI 2004»
13 years 5 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...
BMCBI
2006
106views more  BMCBI 2006»
13 years 5 months ago
Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional nor
Background: Low-level processing and normalization of microarray data are most important steps in microarray analysis, which have profound impact on downstream analysis. Multiple ...
Henrik Bengtsson, Ola Hössjer