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» Gene set analysis for longitudinal gene expression data
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BMCBI
2004
106views more  BMCBI 2004»
13 years 8 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
2007
186views more  BMCBI 2007»
13 years 8 months ago
Modeling human cancer-related regulatory modules by GA-RNN hybrid algorithms
Background: Modeling cancer-related regulatory modules from gene expression profiling of cancer tissues is expected to contribute to our understanding of cancer biology as well as...
Jung-Hsien Chiang, Shih-Yi Chao
ICANN
2010
Springer
13 years 9 months ago
Discovery of Exogenous Variables in Data with More Variables Than Observations
Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations. However, modern datasets including gene expres...
Yasuhiro Sogawa, Shohei Shimizu, Aapo Hyvärin...
BMCBI
2005
116views more  BMCBI 2005»
13 years 8 months ago
Can Zipf's law be adapted to normalize microarrays?
Background: Normalization is the process of removing non-biological sources of variation between array experiments. Recent investigations of data in gene expression databases for ...
Timothy Lu, Christine M. Costello, Peter J. P. Cro...
BMCBI
2005
93views more  BMCBI 2005»
13 years 8 months ago
Identification of novel prognostic markers in cervical intraepithelial neoplasia using LDMAS (LOH Data Management and Analysis S
Background: Detection of Loss of Heterozygosity (LOH) is one of the most common molecular applications in the study of human diseases, in particular cancer. The technique is commo...
Rifat A. Hamoudi, Amina El-Hamidi, Ming-Qing Du