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BMCBI
2006
183views more  BMCBI 2006»
14 years 9 months ago
Mining gene expression data by interpreting principal components
Background: There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many insta...
Joseph C. Roden, Brandon W. King, Diane Trout, Ali...
BIBM
2008
IEEE
212views Bioinformatics» more  BIBM 2008»
15 years 4 months ago
Analysis of Multiplex Gene Expression Maps Obtained by Voxelation
Background: Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelatio...
Li An, Hongbo Xie, Mark H. Chin, Zoran Obradovic, ...
BMCBI
2010
158views more  BMCBI 2010»
14 years 9 months ago
Validation of differential gene expression algorithms: Application comparing fold-change estimation to hypothesis testing
Background: Sustained research on the problem of determining which genes are differentially expressed on the basis of microarray data has yielded a plethora of statistical algorit...
Corey M. Yanofsky, David R. Bickel
83
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KDD
2002
ACM
183views Data Mining» more  KDD 2002»
15 years 10 months ago
E-CAST: A Data Mining Algorithm for Gene Expression Data
Data clustering methods have been proven to be a successful data mining technique in the analysis of gene expression data. The Cluster affinity search technique (CAST) developed b...
Abdelghani Bellaachia, David Portnoy, Yidong Chen,...
79
Voted
FGCN
2008
IEEE
155views Communications» more  FGCN 2008»
14 years 11 months ago
Modeling the Marginal Distribution of Gene Expression with Mixture Models
We report the results of fitting mixture models to the distribution of expression values for individual genes over a broad range of normal tissues, which we call the marginal expr...
Edward Wijaya, Hajime Harada, Paul Horton