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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Vydané v:	Journal of ambient intelligence and humanized computing Ročník 12; číslo 2; s. 2731 - 2750
Hlavní autori:	Dabba, Ali, Tari, Abdelkamel, Meftali, Samy
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021 Springer Nature B.V Springer
Predmet:	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
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