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» Analysis of Variance for Gene Expression Microarray Data
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BMCBI
2007
159views more  BMCBI 2007»
15 years 1 months ago
Detecting differential expression in microarray data: comparison of optimal procedures
Background: Many procedures for finding differentially expressed genes in microarray data are based on classical or modified t-statistics. Due to multiple testing considerations, ...
Elena Perelman, Alexander Ploner, Stefano Calza, Y...
BMCBI
2010
91views more  BMCBI 2010»
15 years 1 months ago
Algorithm-driven Artifacts in median polish summarization of Microarray data
Background: High-throughput measurement of transcript intensities using Affymetrix type oligonucleotide microarrays has produced a massive quantity of data during the last decade....
Federico M. Giorgi, Anthony M. Bolger, Marc Lohse,...
GECCO
2004
Springer
104views Optimization» more  GECCO 2004»
15 years 6 months ago
A Genetic Approach for Gene Selection on Microarray Expression Data
Abstract. Microarrays allow simultaneous measurement of the expression levels of thousands of genes in cells under different physiological or disease states. Because the number of...
Yong-Hyuk Kim, Su-Yeon Lee, Byung Ro Moon
BMCBI
2004
150views more  BMCBI 2004»
15 years 1 months ago
Graph-based iterative Group Analysis enhances microarray interpretation
Background: One of the most time-consuming tasks after performing a gene expression experiment is the biological interpretation of the results by identifying physiologically impor...
Rainer Breitling, Anna Amtmann, Pawel Herzyk
BMCBI
2005
113views more  BMCBI 2005»
15 years 1 months ago
Pathway level analysis of gene expression using singular value decomposition
Background: A promising direction in the analysis of gene expression focuses on the changes in expression of specific predefined sets of genes that are known in advance to be rela...
John K. Tomfohr, Jun Lu, Thomas B. Kepler