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» Combined Gene Selection Methods for Microarray Data Analysis
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158
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BMCBI
2008
159views more  BMCBI 2008»
15 years 2 months ago
Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments
Background: Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increas...
Hongya Zhao, Kwok-Leung Chan, Lee-Ming Cheng, Hong...
122
Voted
ICML
2007
IEEE
16 years 3 months ago
Hybrid huberized support vector machines for microarray classification
The large number of genes and the relatively small number of samples are typical characteristics for microarray data. These characteristics pose challenges for both sample classif...
Li Wang, Ji Zhu, Hui Zou
183
Voted
BMCBI
2011
14 years 9 months ago
A novel approach to the clustering of microarray data via nonparametric density estimation
Background: Cluster analysis is a crucial tool in several biological and medical studies dealing with microarray data. Such studies pose challenging statistical problems due to di...
Riccardo De Bin, Davide Risso
BMCBI
2007
194views more  BMCBI 2007»
15 years 2 months ago
A meta-data based method for DNA microarray imputation
Background: DNA microarray experiments are conducted in logical sets, such as time course profiling after a treatment is applied to the samples, or comparisons of the samples unde...
Rebecka Jörnsten, Ming Ouyang, Hui-Yu Wang
147
Voted
BMCBI
2006
211views more  BMCBI 2006»
15 years 2 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