Variable selection and pattern recognition with gene expression data generated by the microarray technology

DOI10.1016/S0025-5564(01)00103-1zbMath1006.62093WikidataQ30676405 ScholiaQ30676405MaRDI QIDQ1602590

Aniko Szabo, W. L. Carroll, Alexander D. Tsodikov, Kenneth Boucher, Andrej Yu. Yakovlev, Lev B. Klebanov

Publication date: 23 June 2002

Published in: Mathematical Biosciences (Search for Journal in Brave)

zbMATH Keywords

pattern recognitionnonparametric methodsdata adjustmentprobability distance

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40) Genetics and epigenetics (92D10)

Related Items (6)

NORMALITY OF GENE EXPRESSION REVISITED ⋮ Discrimination and scoring using small sets of genes for two-sample microarray data ⋮ Diverse correlation structures in gene expression data and their utility in improving statistical inference ⋮ A permutation test motivated by microarray data analysis ⋮ A NOTE ON CLASSIFICATION OF GENE EXPRESSION DATA USING SUPPORT VECTOR MACHINES ⋮ Identification of potential biomarkers on microarray data using distributed gene selection approach

Cites Work

• Multivariate approach for selecting sets of differentially expressed genes
• Extracting information from cDNA arrays
• ON SELECTING VARIABLES AND ASSESSING THEIR PERFORMANCE IN LINEAR DISCRIMINANT ANALYSIS
• Multivariate empirical characteristic functions
• Variable selection techniques in discriminant analysis: I. Description
• Variable selection techniques in discriminant analysis: II. Allocation
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations
• Mass transhipment problems and ideal metrics
• Gene expression analysis with the parametric bootstrap
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