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» Missing value imputation for microarray gene expression data...
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BMCBI
2008
193views more  BMCBI 2008»
11 years 5 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
2006
211views more  BMCBI 2006»
11 years 5 months ago
Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding s
Background: Gene expression profiling has become a useful biological resource in recent years, and it plays an important role in a broad range of areas in biology. The raw gene ex...
Xian Wang, Ao Li, Zhaohui Jiang, Huanqing Feng
BMCBI
2006
103views more  BMCBI 2006»
11 years 5 months ago
Improving missing value imputation of microarray data by using spot quality weights
Background: Microarray technology has become popular for gene expression profiling, and many analysis tools have been developed for data interpretation. Most of these tools requir...
Peter Johansson, Jari Häkkinen
BMCBI
2008
190views more  BMCBI 2008»
11 years 5 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
2008
104views more  BMCBI 2008»
11 years 5 months ago
Missing value imputation improves clustering and interpretation of gene expression microarray data
Background: Missing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value i...
Johannes Tuikkala, Laura Elo, Olli Nevalainen, Ter...
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