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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...
BIOINFORMATICS
2005
109views more  BIOINFORMATICS 2005»
13 years 7 months ago
Prediction error estimation: a comparison of resampling methods
In genomic studies, thousands of features are collected on relatively few samples. One of the goals of these studies is to build classifiers to predict the outcome of future obser...
Annette M. Molinaro, Richard Simon, Ruth M. Pfeiff...
BMCBI
2007
179views more  BMCBI 2007»
13 years 7 months ago
Gene selection with multiple ordering criteria
Background: A microarray study may select different differentially expressed gene sets because of different selection criteria. For example, the fold-change and p-value are two co...
James J. Chen, Chen-An Tsai, ShengLi Tzeng, Chun-H...
CIBCB
2006
IEEE
13 years 9 months ago
A New Hybrid Approach for Unsupervised Gene Selection
In recent years, unsupervised gene (feature) selection has become an integral part of microarray analysis because of the large number of genes and complexity in biological systems....
Young Bun Kim, Jean Gao
BMCBI
2004
205views more  BMCBI 2004»
13 years 7 months ago
A combinational feature selection and ensemble neural network method for classification of gene expression data
Background: Microarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for...
Bing Liu, Qinghua Cui, Tianzi Jiang, Songde Ma