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Article

Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model

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Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia
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Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah 42353, Saudi Arabia
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Centre for Advanced Data Science, Vellore Institute of Technology, Chennai 600127, India
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Department of Electrical Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
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Department of Computer Science, Liverpool John Moores University, Liverpool L3 3AF, UK
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Author to whom correspondence should be addressed.
Academic Editors: Jerry Chun-Wei Lin, Gautam Srivastava and Stefania Tomasiello
Appl. Sci. 2022, 12(9), 4172; https://doi.org/10.3390/app12094172
Received: 12 March 2022 / Revised: 15 April 2022 / Accepted: 18 April 2022 / Published: 21 April 2022
(This article belongs to the Special Issue Integrated Artificial Intelligence in Data Science)
Microarray data examination is a relatively new technology that intends to determine the proper treatment for various diseases and a precise medical diagnosis by analyzing a massive number of genes in various experimental conditions. The conventional data classification techniques suffer from overfitting and the high dimensionality of gene expression data. Therefore, the feature (gene) selection approach plays a vital role in handling a high dimensionality of data. Data science concepts can be widely employed in several data classification problems, and they identify different class labels. In this aspect, we developed a novel red fox optimizer with deep-learning-enabled microarray gene expression classification (RFODL-MGEC) model. The presented RFODL-MGEC model aims to improve classification performance by selecting appropriate features. The RFODL-MGEC model uses a novel red fox optimizer (RFO)-based feature selection approach for deriving an optimal subset of features. Moreover, the RFODL-MGEC model involves a bidirectional cascaded deep neural network (BCDNN) for data classification. The parameters involved in the BCDNN technique were tuned using the chaos game optimization (CGO) algorithm. Comprehensive experiments on benchmark datasets indicated that the RFODL-MGEC model accomplished superior results for subtype classifications. Therefore, the RFODL-MGEC model was found to be effective for the identification of various classes for high-dimensional and small-scale microarray data. View Full-Text
Keywords: microarray data classification; data science; chaos game optimization; feature selection; deep learning; red fox optimizer microarray data classification; data science; chaos game optimization; feature selection; deep learning; red fox optimizer
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MDPI and ACS Style

Vaiyapuri, T.; Liyakathunisa; Alaskar, H.; Aljohani, E.; Shridevi, S.; Hussain, A. Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model. Appl. Sci. 2022, 12, 4172. https://doi.org/10.3390/app12094172

AMA Style

Vaiyapuri T, Liyakathunisa, Alaskar H, Aljohani E, Shridevi S, Hussain A. Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model. Applied Sciences. 2022; 12(9):4172. https://doi.org/10.3390/app12094172

Chicago/Turabian Style

Vaiyapuri, Thavavel, Liyakathunisa, Haya Alaskar, Eman Aljohani, S. Shridevi, and Abir Hussain. 2022. "Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model" Applied Sciences 12, no. 9: 4172. https://doi.org/10.3390/app12094172

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