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» Combined Gene Selection Methods for Microarray Data Analysis
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BIBE
2007
IEEE
153views Bioinformatics» more  BIBE 2007»
15 years 1 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
2011
14 years 6 months ago
AnyExpress: Integrated toolkit for analysis of cross-platform gene expression data using a fast interval matching algorithm
Background: Cross-platform analysis of gene express data requires multiple, intricate processes at different layers with various platforms. However, existing tools handle only a s...
Jihoon Kim, Kiltesh Patel, Hyunchul Jung, Winston ...
APBC
2003
128views Bioinformatics» more  APBC 2003»
15 years 1 months ago
Machine Learning in DNA Microarray Analysis for Cancer Classification
The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it e...
Sung-Bae Cho, Hong-Hee Won
BMCBI
2007
149views more  BMCBI 2007»
14 years 11 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
BMCBI
2007
143views more  BMCBI 2007»
14 years 11 months ago
Gene selection for classification of microarray data based on the Bayes error
Background: With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, ...
Ji-Gang Zhang, Hong-Wen Deng