New Bio-Marker Gene Discovery Algorithms for Cancer Gene Expression Profile
| Autor: | Hala M. Alshamlan, Nada Almugren |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Computer science SVM Feature extraction Feature selection 02 engineering and technology 03 medical and health sciences Gene expression 0202 electrical engineering electronic engineering information engineering General Materials Science Firefly algorithm bio-inspired Selection algorithm Firefly Selection (genetic algorithm) 030304 developmental biology 0303 health sciences General Engineering Hybrid algorithm Support vector machine Statistical classification ComputingMethodologies_PATTERNRECOGNITION gene expression profile 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Algorithm microarray lcsh:TK1-9971 gene selection |
| Zdroj: | IEEE Access, Vol 7, Pp 136907-136913 (2019) |
| ISSN: | 2169-3536 |
| Popis: | Several hybrid gene selection algorithms for cancer classification that employ bio-inspired evolutionary wrapper algorithm have been proposed in the literature and show good classification accuracy. In our recent previous work, we proposed a new wrapper gene selection method based-on firefly algorithm named FF-SVM. In this work, we will improve the classification performance of FF-SVM algorithm by proposed a new hybrid gene selection algorithm. Our new biomarker gene discovery algorithm for microarray cancer gene expression analysis that integrates f-score filter method with Firefly feature selection method alongside with SVM classifier named FFF-SVM is proposed. The classification accuracy for the selected gene subset is measured by support vector machine SVM classifier with leave-one-out cross validation LOOCV. The evaluation of the FFF-SVM algorithm done by using five 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 classification 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. |
| Databáze: | OpenAIRE |
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