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

Vaiyapuri, T, Liyakathunisa, , Alaskar, H, Aljohani, E, Shridevi, S and Hussain, A (2022) Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model. Applied Sciences, 12 (9). ISSN 2076-3417

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Open Access URL: https://doi.org/10.3390/app12094172 (Published version)


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.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Publisher: MDPI AG
SWORD Depositor: A Symplectic
Date Deposited: 10 Jan 2023 11:19
Last Modified: 10 Jan 2023 11:30
DOI or ID number: 10.3390/app12094172
URI: https://researchonline.ljmu.ac.uk/id/eprint/18589
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