New Bio-Marker Gene Discovery Algorithms for Cancer Gene Expression Profile

Journal Article
المجلة \ الصحيفة: 
IEEE Access
رقم الإصدار السنوي: 
مستخلص المنشور: 

Several hybrid gene selection algorithms for cancer classication that employ bio-inspired
evolutionary wrapper algorithm have been proposed in the literature and show good classication accuracy.
In our recent previous work, we proposed a new wrapper gene selection method based-on rey algorithm
named FF-SVM. In this work, we will improve the classication performance of FF-SVM algorithm by
proposed a newhybrid gene selection algorithm. Our newbiomarker gene discovery algorithm for microarray
cancer gene expression analysis that integrates f-score lter method with Firey feature selection method
alongside with SVM classier named FFF-SVM is proposed. The classication accuracy for the selected
gene subset is measured by support vector machine SVM classier with leave-one-out cross validation
LOOCV. The evaluation of the FFF-SVM algorithm done by using ve benchmark microarray datasets
of binary and multi class. To show result validation of the proposed we compare it with other related
state-of-the-art algorithms. The experiment proves that the FFF-SVM outperform other hybrid algorithm
in terms of high classication accuracy and low number of selected genes. In addition, we compare the
proposed algorithm with previously proposed wrapper-based gene selection algorithm FF-SVM. The result
show that the hybrid-based algorithm shoe higher performance than wrapper based. The proposed algorithm
is an improvement of our previous proposed algorithm.

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