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» Analysis of Variance for Gene Expression Microarray Data
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GECCO
2003
Springer
127views Optimization» more  GECCO 2003»
15 years 6 months ago
Complex Function Sets Improve Symbolic Discriminant Analysis of Microarray Data
Abstract. Our ability to simultaneously measure the expression levels of thousands of genes in biological samples is providing important new opportunities for improving the diagnos...
David M. Reif, Bill C. White, Nancy Olsen, Thomas ...
EVOW
2004
Springer
15 years 7 months ago
Evolutionary Search of Thresholds for Robust Feature Set Selection: Application to the Analysis of Microarray Data
Abstract. We deal with two important problems in pattern recognition that arise in the analysis of large datasets. While most feature subset selection methods use statistical techn...
Carlos Cotta, Christian Sloper, Pablo Moscato
IJDMB
2008
132views more  IJDMB 2008»
15 years 1 months ago
A Bayesian framework for knowledge driven regression model in micro-array data analysis
: This paper addresses the sparse data problem in the linear regression model, namely the number of variables is significantly larger than the number of the data points for regress...
Rong Jin, Luo Si, Christina Chan
BMCBI
2007
134views more  BMCBI 2007»
15 years 1 months ago
Nearest Neighbor Networks: clustering expression data based on gene neighborhoods
Background: The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both indiv...
Curtis Huttenhower, Avi I. Flamholz, Jessica N. La...
BMCBI
2008
135views more  BMCBI 2008»
15 years 1 months ago
Using Generalized Procrustes Analysis (GPA) for normalization of cDNA microarray data
Background: Normalization is essential in dual-labelled microarray data analysis to remove nonbiological variations and systematic biases. Many normalization methods have been use...
Huiling Xiong, Dapeng Zhang, Christopher J. Martyn...