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GECCO
2004
Springer
104views Optimization» more  GECCO 2004»
14 years 4 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
CSB
2002
IEEE
169views Bioinformatics» more  CSB 2002»
14 years 3 months ago
Bayesian Network and Nonparametric Heteroscedastic Regression for Nonlinear Modeling of Genetic Network
We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network con...
Seiya Imoto, SunYong Kim, Takao Goto, Sachiyo Abur...
ACIIDS
2010
IEEE
170views Database» more  ACIIDS 2010»
13 years 9 months ago
On the Effectiveness of Gene Selection for Microarray Classification Methods
Microarray data usually contains a high level of noisy gene data, the noisy gene data include incorrect, noise and irrelevant genes. Before Microarray data classification takes pla...
Zhongwei Zhang, Jiuyong Li, Hong Hu, Hong Zhou
BMCBI
2002
188views more  BMCBI 2002»
13 years 10 months ago
The limit fold change model: A practical approach for selecting differentially expressed genes from microarray data
Background: The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic an...
David M. Mutch, Alvin Berger, Robert Mansourian, A...
CANDC
2005
ACM
13 years 10 months ago
Gene selection from microarray data for cancer classification - a machine learning approach
A DNA microarray can track the expression levels of thousands of genes simultaneously. Previous research has demonstrated that this technology can be useful in the classification ...
Yu Wang 0008, Igor V. Tetko, Mark A. Hall, Eibe Fr...