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» Missing value imputation for microarray gene expression data...
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BMCBI
2007
149views more  BMCBI 2007»
13 years 4 months ago
Robust imputation method for missing values in microarray data
Background: When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis canno...
Dankyu Yoon, Eun-Kyung Lee, Taesung Park
BIOINFORMATICS
2007
190views more  BIOINFORMATICS 2007»
13 years 4 months ago
Towards clustering of incomplete microarray data without the use of imputation
Motivation: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by...
Dae-Won Kim, Ki Young Lee, Kwang H. Lee, Doheon Le...
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
2006
116views more  BMCBI 2006»
13 years 4 months ago
Integrative missing value estimation for microarray data
Background: Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performan...
Jianjun Hu, Haifeng Li, Michael S. Waterman, Xiang...
AINA
2008
IEEE
13 years 11 months ago
Missing Value Estimation for Time Series Microarray Data Using Linear Dynamical Systems Modeling
The analysis of gene expression time series obtained from microarray experiments can be effectively exploited to understand a wide range of biological phenomena from the homeostat...
Connie Phong, Raul Singh