New Bio-Marker Gene Discovery Algorithms for Cancer Gene Expression Profile
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.
The emergence of DNA Microarray technology has enabled researchers to analyze the
expression level of thousands of genes simultaneously. The Microarray data analysis is the process of nding…
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…