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Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications

Areej A. Malibari1, Reem M. Alshehri2, Fahd N. Al-Wesabi3, Noha Negm3, Mesfer Al Duhayyim4, Anwer Mustafa Hilal5,*, Ishfaq Yaseen5, Abdelwahed Motwakel5

1 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
3 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
4 Department of Natural and Applied Sciences, College of Community-Aflaj, Prince Sattam bin Abdulaziz University, Saudi Arabia
5 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Anwer Mustafa Hilal. Email: email

Computers, Materials & Continua 2022, 73(2), 4277-4290.


In bioinformatics applications, examination of microarray data has received significant interest to diagnose diseases. Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes. Microarray data classification incorporates multiple disciplines such as bioinformatics, machine learning (ML), data science, and pattern classification. This paper designs an optimal deep neural network based microarray gene expression classification (ODNN-MGEC) model for bioinformatics applications. The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale. Besides, improved fruit fly optimization (IFFO) based feature selection technique is used to reduce the high dimensionality in the biomedical data. Moreover, deep neural network (DNN) model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search (SOS) algorithm. The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes. For examining the improved outcomes of the ODNN-MGEC technique, a wide ranging experimental analysis is made against benchmark datasets. The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.


Cite This Article

APA Style
Malibari, A.A., Alshehri, R.M., Al-Wesabi, F.N., Negm, N., Duhayyim, M.A. et al. (2022). Deep learning enabled microarray gene expression classification for data science applications. Computers, Materials & Continua, 73(2), 4277-4290.
Vancouver Style
Malibari AA, Alshehri RM, Al-Wesabi FN, Negm N, Duhayyim MA, Hilal AM, et al. Deep learning enabled microarray gene expression classification for data science applications. Comput Mater Contin. 2022;73(2):4277-4290
IEEE Style
A.A. Malibari et al., "Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications," Comput. Mater. Contin., vol. 73, no. 2, pp. 4277-4290. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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