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CSB
2005
IEEE
143views Bioinformatics» more  CSB 2005»
14 years 1 months ago
Multivariate gene selection: Does it help
When building predictors of disease state based on gene expression data, gene selection is performed in order to achieve a good performance and to identify a relevant subset of ge...
Carmen Lai, Marcel J. T. Reinders
BMCBI
2008
134views more  BMCBI 2008»
13 years 7 months ago
Identification of transcription factor contexts in literature using machine learning approaches
Background: Availability of information about transcription factors (TFs) is crucial for genome biology, as TFs play a central role in the regulation of gene expression. While man...
Hui Yang, Goran Nenadic, John A. Keane
BMCBI
2007
140views more  BMCBI 2007»
13 years 7 months ago
Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets
Background: Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles...
Michael Gormley, William Dampier, Adam Ertel, Bilg...
BIOINFORMATICS
2005
134views more  BIOINFORMATICS 2005»
13 years 7 months ago
Outcome signature genes in breast cancer: is there a unique set?
Motivation: direct bearing whose expres Sorlie et al., 2 gene sets is a diseases (Lossos et al., 2004; Miklos and Maleszka, 2004), and the variables that could account questions i...
Liat Ein-Dor, Itai Kela, Gad Getz, David Givol, Ey...
CVPR
2004
IEEE
14 years 9 months ago
Feature Selection for Classifying High-Dimensional Numerical Data
Classifying high-dimensional numerical data is a very challenging problem. In high dimensional feature spaces, the performance of supervised learning methods suffer from the curse...
Yimin Wu, Aidong Zhang