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BMCBI
2010
135views more  BMCBI 2010»
13 years 5 months ago
Simple and flexible classification of gene expression microarrays via Swirls and Ripples
Background: A simple classification rule with few genes and parameters is desirable when applying a classification rule to new data. One popular simple classification rule, diagon...
Stuart G. Baker
BMCBI
2008
128views more  BMCBI 2008»
13 years 5 months ago
Improving the prediction accuracy in classification using the combined data sets by ranks of gene expressions
Background: The information from different data sets experimented under different conditions may be inconsistent even though they are performed with the same research objectives. ...
Ki-Yeol Kim, Dong Hyuk Ki, Hei-Cheul Jeung, Hyun C...
BMCBI
2006
129views more  BMCBI 2006»
13 years 5 months ago
Identifying genes that contribute most to good classification in microarrays
Background: The goal of most microarray studies is either the identification of genes that are most differentially expressed or the creation of a good classification rule. The dis...
Stuart G. Baker, Barnett S. Kramer
BMCBI
2008
105views more  BMCBI 2008»
13 years 5 months ago
SIGMA2: A system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes
Background: High throughput microarray technologies have afforded the investigation of genomes, epigenomes, and transcriptomes at unprecedented resolution. However, software packa...
Raj Chari, Bradley P. Coe, Craig Wedseltoft, Marie...
BMCBI
2007
143views more  BMCBI 2007»
13 years 5 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