Gene Selection for Cancer Classification through Ensemble of Methods

2009 ◽  
pp. 507-516 ◽  
Author(s):  
Artur Wilinski ◽  
Stanislaw Osowski ◽  
Krzysztof Siwek
Keyword(s):  
Gene Selection ◽  
Cancer Classification ◽  
Selection For

scholarly journals Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization

2018 ◽  
Vol 8 (9) ◽  
pp. 1569 ◽  
Author(s):  
Shengbing Wu ◽  
Hongkun Jiang ◽  
Haiwei Shen ◽  
Ziyi Yang
Keyword(s):  
Logistic Regression ◽  
Gene Selection ◽  
Classification Performance ◽  
Cancer Classification ◽  
Sparse Logistic Regression ◽  
The Subject ◽  
Selection For ◽  
Microarray Datasets ◽  
Sparse Methods

In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help in the classification of different types of cancer and improve the prediction accuracy. Recently, regularized logistic regression using the L 1 regularization has been successfully applied in high-dimensional cancer classification to tackle both the estimation of gene coefficients and the simultaneous performance of gene selection. However, the L 1 has a biased gene selection and dose not have the oracle property. To address these problems, we investigate L 1 / 2 regularized logistic regression for gene selection in cancer classification. Experimental results on three DNA microarray datasets demonstrate that our proposed method outperforms other commonly used sparse methods ( L 1 and L E N ) in terms of classification performance.

PCA-based gene selection for cancer classification

2018 ◽  
Author(s):  
K.R Kavitha ◽  
Aiswarya V Ram ◽  
S Anandu ◽  
S Karthik ◽  
Sreeja Kailas ◽  
...  
Keyword(s):  
Gene Selection ◽  
Cancer Classification ◽  
Selection For
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