A New Algorithm for Cancer Biomarker Gene Detection Using Harris Hawks Optimization
This paper presents two novel swarm intelligence algorithms for gene selection, HHO-SVM and HHO-KNN. Both of these algorithms are based on Harris Hawks Optimization (HHO), one in conjunction with support vector machines (SVM) and the other in conjunction with <i>k</i>-nearest neighbors (...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7273 |
| _version_ | 1827653078939074560 |
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| author | Halah AlMazrua Hala AlShamlan |
| author_facet | Halah AlMazrua Hala AlShamlan |
| author_sort | Halah AlMazrua |
| collection | DOAJ |
| description | This paper presents two novel swarm intelligence algorithms for gene selection, HHO-SVM and HHO-KNN. Both of these algorithms are based on Harris Hawks Optimization (HHO), one in conjunction with support vector machines (SVM) and the other in conjunction with <i>k</i>-nearest neighbors (<i>k</i>-NN). In both algorithms, the goal is to determine a small gene subset that can be used to classify samples with a high degree of accuracy. The proposed algorithms are divided into two phases. To obtain an accurate gene set and to deal with the challenge of high-dimensional data, the redundancy analysis and relevance calculation are conducted in the first phase. To solve the gene selection problem, the second phase applies SVM and <i>k</i>-NN with leave-one-out cross-validation. A performance evaluation was performed on six microarray data sets using the two proposed algorithms. A comparison of the two proposed algorithms with several known algorithms indicates that both of them perform quite well in terms of classification accuracy and the number of selected genes. |
| first_indexed | 2024-03-09T21:11:01Z |
| format | Article |
| id | doaj.art-53b6854057314274999d315b3211b136 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:11:01Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-53b6854057314274999d315b3211b1362023-11-23T21:46:25ZengMDPI AGSensors1424-82202022-09-012219727310.3390/s22197273A New Algorithm for Cancer Biomarker Gene Detection Using Harris Hawks OptimizationHalah AlMazrua0Hala AlShamlan1Information Technology Department, College of Computer and Information Sciences, King Saud University (KSU), Riyadh 11451, Saudi ArabiaInformation Technology Department, College of Computer and Information Sciences, King Saud University (KSU), Riyadh 11451, Saudi ArabiaThis paper presents two novel swarm intelligence algorithms for gene selection, HHO-SVM and HHO-KNN. Both of these algorithms are based on Harris Hawks Optimization (HHO), one in conjunction with support vector machines (SVM) and the other in conjunction with <i>k</i>-nearest neighbors (<i>k</i>-NN). In both algorithms, the goal is to determine a small gene subset that can be used to classify samples with a high degree of accuracy. The proposed algorithms are divided into two phases. To obtain an accurate gene set and to deal with the challenge of high-dimensional data, the redundancy analysis and relevance calculation are conducted in the first phase. To solve the gene selection problem, the second phase applies SVM and <i>k</i>-NN with leave-one-out cross-validation. A performance evaluation was performed on six microarray data sets using the two proposed algorithms. A comparison of the two proposed algorithms with several known algorithms indicates that both of them perform quite well in terms of classification accuracy and the number of selected genes.https://www.mdpi.com/1424-8220/22/19/7273bio-inspired algorithmsbioinformaticscancer classificationevolutionary algorithmfeature selectiongene expression |
| spellingShingle | Halah AlMazrua Hala AlShamlan A New Algorithm for Cancer Biomarker Gene Detection Using Harris Hawks Optimization Sensors bio-inspired algorithms bioinformatics cancer classification evolutionary algorithm feature selection gene expression |
| title | A New Algorithm for Cancer Biomarker Gene Detection Using Harris Hawks Optimization |
| title_full | A New Algorithm for Cancer Biomarker Gene Detection Using Harris Hawks Optimization |
| title_fullStr | A New Algorithm for Cancer Biomarker Gene Detection Using Harris Hawks Optimization |
| title_full_unstemmed | A New Algorithm for Cancer Biomarker Gene Detection Using Harris Hawks Optimization |
| title_short | A New Algorithm for Cancer Biomarker Gene Detection Using Harris Hawks Optimization |
| title_sort | new algorithm for cancer biomarker gene detection using harris hawks optimization |
| topic | bio-inspired algorithms bioinformatics cancer classification evolutionary algorithm feature selection gene expression |
| url | https://www.mdpi.com/1424-8220/22/19/7273 |
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