Open Access

ARTICLE

Hybrid Approach for Taxonomic Classification Based on Deep Learning

Naglaa. F. Soliman^1,*, Samia M. Abd-Alhalem², Walid El-Shafai², Salah Eldin S. E. Abdulrahman³, N. Ismaiel³, El-Sayed M. El-Rabaie², Abeer D. Algarni¹, Fatimah Algarni⁴, Amel A. Alhussan⁵, Fathi E. Abd El-Samie^1,2

1 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
2 Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menoufia, 32952, Egypt
3 Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menoufia, 32952, Egypt
4 Ministry of Education, Riyadh, Saudi Arabia
5 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

* Corresponding Author: Naglaa. F. Soliman. Email:

Intelligent Automation & Soft Computing 2022, 32(3), 1881-1891. https://doi.org/10.32604/iasc.2022.017683

Received 07 February 2021; Accepted 01 September 2021; Issue published 09 December 2021

Abstract

Recently, deep learning has opened a remarkable research direction in the track of bioinformatics, especially for the applications that need classification and regression. With deep learning techniques, DNA sequences can be classified with high accuracy. Firstly, a DNA sequence should be represented, numerically. After that, DNA features are extracted from the numerical representations based on deep learning techniques to improve the classification process. Recently, several architectures have been developed based on deep learning for DNA sequence classification. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are the default deep learning architectures used for this task. This paper presents a hybrid module that combines a CNN with an RNN for DNA classification. The CNN is used for feature extraction, and this is followed by a subsampling layer, while the RNN is trained for classifying bacteria into taxonomic levels. Besides, a wavelet-based pooling strategy is adopted in the subsampling layer, because the wavelet transform with down-sampling allows signal compression, while maintaining the most discriminative features of the signal. The proposed hybrid module is compared with a CNN based on Random Projection (RP) and an RNN based on histogram-oriented gradient features. The simulation results show that the hybrid module has the best performance among other ones.

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Keywords

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