DNA Sequences Classification with Deep Learning: A Survey | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 6, Volume 30, Issue 1, January 2021, Page 41-51 PDF (877.44 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2021.146090 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Samia M. Abd –Alhalem1; El-Sayed M. El-Rabaie1; Naglaa. F. Soliman2; Salah Eldin S. E. Abdulrahman3; Nabil A. Ismail3; Fathi E. Abd El-samie1 | ||||
| 1Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf | ||||
| 2Faculty of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia | ||||
| 3Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf | ||||
| 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. | ||||
| References | ||||
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