Spectral Features Based on Bidirectional Long Short-Term Memory for DNA Classification | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 55, Volume 28, ICEEM2019-Special Issue, 2019, Page 183-188 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2019.77008 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Samia M. Abd-Alhalem1; Naglaa F. Soliman2; Salah Eldin, S. E. Abd Elrahman3; Nabil A. Ismail4; El-Sayed M. El-Rabaie5; Fathi E. Abd El-Samie6 | ||||
| 1dept. Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University: Menouf, Egypt | ||||
| 2dept. of Electronics and Communication Faculty of Engineering, Zagazig University, Zagazig, Egypt Faculty of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia | ||||
| 3dept. Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University Menouf, Egypt | ||||
| 4dept. of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University Menouf, Egypt | ||||
| 5dept. Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University: Menouf, Egypt | ||||
| 6dept. Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University: Menouf, Egypt | ||||
| Abstract | ||||
| the analyzing hidden features of DNA sequence is main challenge in bioinformatics. Since learning from DNA sequences based on analytical approaches to identify the hidden patterns provides a vital role in various genomic applications. In classification tasks, Re-current neural network with Bidirectional Long Short-Term Memory (BLSTM) is usually used for sequential data that is strongly dependent on the feature's extraction stage. Recently, digital signal processing (DSP) techniques such as spectral transformations has been used in genomic data for extracting the hidden features and periodicities within the DNA fragments. The objective of this paper is comparing different spectral transformations of DNA sequences such as Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) based on BLSTM to achieve high performance in the taxonomic classification of bacteria. The reason for applying these transformations emerges from its wide and effective use for extracting features, decorrelation, ordering and dimensionality reduction purposes in the fields of speech and image processing. Evaluation metrics such as F1 score and accuracy show that DWT features give surpassing performance compared with other features. | ||||
| Keywords | ||||
| Bidirectional Long Short-Term Memory (BLSTM); Discrete Cosine Transform (DCT); Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) | ||||
| References | ||||
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