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JCB
2002
70views more  JCB 2002»
13 years 7 months ago
Strong Feature Sets from Small Samples
For small samples, classi er design algorithms typically suffer from over tting. Given a set of features, a classi er must be designed and its error estimated. For small samples, ...
Seungchan Kim, Edward R. Dougherty, Junior Barrera...
ISBRA
2007
Springer
14 years 1 months ago
Noise-Based Feature Perturbation as a Selection Method for Microarray Data
Abstract. DNA microarrays can monitor the expression levels of thousands of genes simultaneously, providing the opportunity for the identification of genes that are differentiall...
Li Chen, Dmitry B. Goldgof, Lawrence O. Hall, Stev...
BMCBI
2005
144views more  BMCBI 2005»
13 years 7 months ago
Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in
Background: Comparison of data produced on different microarray platforms often shows surprising discordance. It is not clear whether this discrepancy is caused by noisy data or b...
Scott L. Carter, Aron C. Eklund, Brigham H. Mecham...
PRL
2006
130views more  PRL 2006»
13 years 7 months ago
Efficient huge-scale feature selection with speciated genetic algorithm
With increasing interest in bioinformatics, sophisticated tools are required to efficiently analyze gene information. The classification of gene expression profiles is crucial in ...
Jin-Hyuk Hong, Sung-Bae Cho
BMCBI
2006
198views more  BMCBI 2006»
13 years 7 months ago
Gene selection and classification of microarray data using random forest
Background: Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of ...
Ramón Díaz-Uriarte, Sara Alvarez de ...