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» Integrative missing value estimation for microarray data
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ICPR
2004
IEEE
14 years 5 months ago
Missing Microarray Data Estimation Based on Projection onto Convex Sets Method
DNA microarrays have gained widespread uses in biological studies. Missing values in a microarray experiment must be estimated before further analysis. In this paper, we propose a...
Alan Wee-Chung Liew, Hong Yan, Xiangchao Gan
BIBE
2007
IEEE
153views Bioinformatics» more  BIBE 2007»
13 years 6 months ago
Combined expression data with missing values and gene interaction network analysis: a Markovian integrated approach
—DNA microarray technologies provide means for monitoring in the order of tens of thousands of gene expression levels quantitatively and simultaneously. However data generated in...
Juliette Blanchet, Matthieu Vignes
BMCBI
2008
193views more  BMCBI 2008»
13 years 4 months ago
Missing value imputation for microarray gene expression data using histone acetylation information
Background: It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile ...
Qian Xiang, Xianhua Dai, Yangyang Deng, Caisheng H...
BMCBI
2008
190views more  BMCBI 2008»
13 years 4 months ago
Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes
Background: Gene expression data frequently contain missing values, however, most downstream analyses for microarray experiments require complete data. In the literature many meth...
Guy N. Brock, John R. Shaffer, Richard E. Blakesle...
BMCBI
2004
113views more  BMCBI 2004»
13 years 4 months ago
Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering
Background: Microarray technologies produced large amount of data. The hierarchical clustering is commonly used to identify clusters of co-expressed genes. However, microarray dat...
Alexandre G. de Brevern, Serge A. Hazout, Alain Ma...