Gene Subset Selection for Cancer Classification Using Statsitical and Rough Set Approach

2012 ◽  
pp. 294-302 ◽  
Author(s):  
Asit Kumar Das ◽  
Soumen Kumar Pati
Keyword(s):  
Rough Set ◽  
Subset Selection ◽  
Cancer Classification ◽  
Gene Subset ◽  
Selection For

scholarly journals An Improved Parallelized mRMR for Gene Subset Selection in Cancer Classification

2017 ◽  
Vol 7 (4-2) ◽  
pp. 1595 ◽  
Author(s):  
Rohani Mohammad Kusairi ◽  
Kohbalan Moorthy ◽  
Habibollah Haron ◽  
Mohd Saberi Mohamad ◽  
Suhaimi Napis ◽  
...  
Keyword(s):  
Subset Selection ◽  
Cancer Classification ◽  
Gene Subset

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.

Gene subset selection in microarray data using entropic filtering for cancer classification

2009 ◽  
Vol 26 (1) ◽  
pp. 113-124 ◽  
Author(s):  
Félix F. González Navarro ◽  
Lluís A. Belanche Muñoz
Keyword(s):  
Microarray Data ◽  
Subset Selection ◽  
Cancer Classification ◽  
Gene Subset
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