Feature Selection and Ranking of Key Genes for Tumor Classification: Using Microarray Gene Expression Data

2006 ◽  
pp. 951-961 ◽  
Author(s):  
Srinivas Mukkamala ◽  
Qingzhong Liu ◽  
Rajeev Veeraghattam ◽  
Andrew H. Sung
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Microarray Gene Expression Data ◽  
Tumor Classification ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Microarray Gene ◽  
Key Genes ◽  
Selection And Ranking

scholarly journals Optimization Based Tumor Classification from Microarray Gene Expression Data

PLoS ONE ◽  
2011 ◽  
Vol 6 (2) ◽  
pp. e14579 ◽  
Author(s):  
Onur Dagliyan ◽  
Fadime Uney-Yuksektepe ◽  
I. Halil Kavakli ◽  
Metin Turkay
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Microarray Gene Expression Data ◽  
Tumor Classification ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Microarray Gene

Hybrid Feature Selection Algorithm mRMR-ICA for Cancer Classification from Microarray Gene Expression Data

2018 ◽  
Vol 21 (6) ◽  
pp. 420-430 ◽  
Author(s):  
Shuaiqun Wang ◽  
Wei Kong ◽  
Aorigele ◽  
Jin Deng ◽  
Shangce Gao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Classification Accuracy ◽  
Microarray Gene Expression Data ◽  
Cancer Classification ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Redundant Genes ◽  
Microarray Gene

Aims and Objective: Redundant information of microarray gene expression data makes it difficult for cancer classification. Hence, it is very important for researchers to find appropriate ways to select informative genes for better identification of cancer. This study was undertaken to present a hybrid feature selection method mRMR-ICA which combines minimum redundancy maximum relevance (mRMR) with imperialist competition algorithm (ICA) for cancer classification in this paper. Materials and Methods: The presented algorithm mRMR-ICA utilizes mRMR to delete redundant genes as preprocessing and provide the small datasets for ICA for feature selection. It will use support vector machine (SVM) to evaluate the classification accuracy for feature genes. The fitness function includes classification accuracy and the number of selected genes. Results: Ten benchmark microarray gene expression datasets are used to test the performance of mRMR-ICA. Experimental results including the accuracy of cancer classification and the number of informative genes are improved for mRMR-ICA compared with the original ICA and other evolutionary algorithms. Conclusion: The comparison results demonstrate that mRMR-ICA can effectively delete redundant genes to ensure that the algorithm selects fewer informative genes to get better classification results. It also can shorten calculation time and improve efficiency.

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