DNA Sequences Classification with Deep Learning: A Survey

Abd –Alhalem, Samia M.; El-Rabaie, El-Sayed M.; Soliman, Naglaa. F.; Abdulrahman, Salah Eldin S. E.; Ismail, Nabil A.; Abd El-samie, Fathi E.

doi:10.21608/mjeer.2021.146090

DNA Sequences Classification with Deep Learning: A Survey

Document Type : Original Article

Authors

¹ Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf

² Faculty of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

³ Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf

10.21608/mjeer.2021.146090

Abstract

Deep learning (DL) methods have been
achieving amazing results in solving a variety of
problems in many different fields especially in the area
of big data. With the advances of the big data era in
bioinformatics, applying DL techniques, the DNA
sequences can be classified with accurate and scalable
prediction. The strength of DL methods come from the
development of software and hardware, such as
processing abilities graphical processing units (GPU) for
the hardware and new learning or inference algorithms
for the software, which reducing the main primary
difficulties that faced the training process. In This work,
we start from the previous classification methods such as
alignment methods pointing out the problems, which are
face to use these methods.After that, we demonstrate
deep learning, from artificial neural networks to hyper
parameter tuning, and the most recent state-of-the-art
DL architectures used in DNA classification. After that,
the paper ended with limitations and suggestions.

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