Hybridization of Genetic and Quantum Algorithm for gene selection and classification of Microarray data

Author(s):  
Allani Abderrahim ◽  
El-Ghazali Talbi ◽  
Mellouli Khaled
2012 ◽  
Vol 23 (02) ◽  
pp. 431-444 ◽  
Author(s):  
ALLANI ABDERRAHIM ◽  
EL-GHAZALI TALBI ◽  
MELLOULI KHALED

In this work, we hybridize the Genetic Quantum Algorithm with the Support Vector Machines classifier for gene selection and classification of high dimensional Microarray Data. We named our algorithm GQA SVM. Its purpose is to identify a small subset of genes that could be used to separate two classes of samples with high accuracy. A comparison of the approach with different methods of literature, in particular GA SVM and PSO SVM [2], was realized on six different datasets issued of microarray experiments dealing with cancer (leukemia, breast, colon, ovarian, prostate, and lung) and available on Web. The experiments clearified the very good performances of the method. The first contribution shows that the algorithm GQA SVM is able to find genes of interest and improve the classification on a meaningful way. The second important contribution consists in the actual discovery of new and challenging results on datasets used.


Author(s):  
Shinn-Ying Ho ◽  
Chong-Cheng Lee ◽  
Hung-Ming Chen ◽  
Hui-Ling Huang

2011 ◽  
Vol 4 (2) ◽  
Author(s):  
Huey Fang Ong ◽  
Norwati Mustapha ◽  
Md. Nasir Sulaiman

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