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
Hauptverfasser:	Dabba, Ali, Tari, Abdelkamel, Meftali, Samy
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021 Springer Nature B.V Springer
Schlagworte:	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 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
ISSN:	1868-5137, 1868-5145
Online-Zugang:	Volltext
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