K-means+ Method for Improving Gene Selection for Classification of Microarray Data

2006 ◽  
Author(s):  
Heng Huang ◽  
Rong Zhang ◽  
Fei Xiong ◽  
F. Makedon ◽  
Li Shen ◽  
...  
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Selection For

Efficient Gene Selection for Classification of Microarray Data

2005 ◽  
Author(s):  
Shinn-Ying Ho ◽  
Chong-Cheng Lee ◽  
Hung-Ming Chen ◽  
Hui-Ling Huang
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Efficient Gene ◽  
Selection For

scholarly journals Integrative Gene Selection for Classification of Microarray Data

2011 ◽  
Vol 4 (2) ◽  
Author(s):  
Huey Fang Ong ◽  
Norwati Mustapha ◽  
Md. Nasir Sulaiman
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document