Gene Selection for Cancer Classification from Microarray Data Using Data Overlap Measure

2018 ◽  
Author(s):  
Saeed Sarbazi-Azad ◽  
Mohammad Saniee Abadeh
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Cancer Classification ◽  
Selection For ◽  
Using Data

scholarly journals Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Simin Hu ◽  
J. Sunil Rao
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Real Data ◽  
Cancer Classification ◽  
Simulation Studies ◽  
Selection Methods ◽  
Gene Subset ◽  
High Quality ◽  
Eigenvalue Ratio ◽  
Selection For

In gene selection for cancer classification using microarray data, we define an eigenvalue-ratio statistic to measure a gene's contribution to the joint discriminability when this gene is included into a set of genes. Based on this eigenvalue-ratio statistic, we define a novel hypothesis testing for gene statistical redundancy and propose two gene selection methods. Simulation studies illustrate the agreement between statistical redundancy testing and gene selection methods. Real data examples show the proposed gene selection methods can select a compact gene subset which can not only be used to build high quality cancer classifiers but also show biological relevance.

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.

Gene selection for microarray data classification via adaptive hypergraph embedded dictionary learning

Gene ◽  
2019 ◽  
Vol 706 ◽  
pp. 188-200 ◽  
Author(s):  
Xiao Zheng ◽  
Wenyang Zhu ◽  
Chang Tang ◽  
Minhui Wang
Keyword(s):  
Microarray Data ◽  
Dictionary Learning ◽  
Gene Selection ◽  
Data Classification ◽  
Selection For
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