Hybridization of Moth flame optimization algorithm and quantum computing for gene selection in microarray data
Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer....
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| Veröffentlicht in: | Journal of ambient intelligence and humanized computing Jg. 12; H. 2; S. 2731 - 2750 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2021
Springer Nature B.V Springer |
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| ISSN: | 1868-5137, 1868-5145 |
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| Abstract | Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test QMFOA on thirteen microarray datasets (six binary-class and seven multi-class) to evaluate and compare the classification accuracy and the number of genes selected by the QMFOA against many recently published algorithms. Experimental results show that QMFOA provides greater classification accuracy and the ability to reduce the number of selected genes compared to the other algorithms. |
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| AbstractList | Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test QMFOA on thirteen microarray datasets (six binary-class and seven multi-class) to evaluate and compare the classification accuracy and the number of genes selected by the QMFOA against many recently published algorithms. Experimental results show that QMFOA provides greater classification accuracy and the ability to reduce the number of selected genes compared to the other algorithms. |
| Author | Tari, Abdelkamel Dabba, Ali Meftali, Samy |
| Author_xml | – sequence: 1 givenname: Ali orcidid: 0000-0001-5602-574X surname: Dabba fullname: Dabba, Ali email: ali.dabba@univ-msila.dz organization: Computer Science Department, Faculty of Mathematics and Computer Science, Mohamed Boudiaf University, Computer Science Department, Faculty of Sciences, Abderrahmane Mira University, Research center in Computer Science, Signal and Automatic Control of Lille-CRIStAL – sequence: 2 givenname: Abdelkamel surname: Tari fullname: Tari, Abdelkamel organization: Computer Science Department, Faculty of Sciences, Abderrahmane Mira University, Medical Computing Laboratory-LIMED – sequence: 3 givenname: Samy surname: Meftali fullname: Meftali, Samy organization: University of Lille, Research center in Computer Science, Signal and Automatic Control of Lille-CRIStAL |
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| Keywords | Feature selection Quantum computing Microarray data Bio-inspired algorithms Optimization algorithms Swarm intelligence Moth flame optimization algorithm Evolutionary algorithms Gene expression Molecular biology Cancer classification |
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| SubjectTerms | Accuracy Algorithms Artificial Intelligence Bioinformatics Cancer Classification Computational Intelligence Computer Science Computers Engineering Feature selection Gene expression Genes Genetic algorithms Genomics Neural networks Optimization Optimization algorithms Original Research Quantum computing Redundancy Robotics and Automation Support vector machines Swarm intelligence User Interfaces and Human Computer Interaction |
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| Title | Hybridization of Moth flame optimization algorithm and quantum computing for gene selection in microarray data |
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