DeepPGD: A Deep Learning Model for DNA Methylation Prediction Using Temporal Convolution, BiLSTM, and Attention Mechanism
Article
Teragawa, Shoryu, Wang, Lei and Liu, Yi. 2024. "DeepPGD: A Deep Learning Model for DNA Methylation Prediction Using Temporal Convolution, BiLSTM, and Attention Mechanism." International Journal of Molecular Sciences. 25 (15). https://doi.org/10.3390/ijms25158146
| Article Title | DeepPGD: A Deep Learning Model for DNA Methylation Prediction Using Temporal Convolution, BiLSTM, and Attention Mechanism |
|---|---|
| ERA Journal ID | 41930 |
| Article Category | Article |
| Authors | Teragawa, Shoryu, Wang, Lei and Liu, Yi |
| Journal Title | International Journal of Molecular Sciences |
| Journal Citation | 25 (15) |
| Article Number | 8146 |
| Number of Pages | 15 |
| Year | 2024 |
| Publisher | MDPI AG |
| Place of Publication | Switzerland |
| ISSN | 1422-0067 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/ijms25158146 |
| Web Address (URL) | https://www.mdpi.com/1422-0067/25/15/8146 |
| Abstract | As part of the field of DNA methylation identification, this study tackles the challenge of enhancing recognition performance by introducing a specialized deep learning framework called DeepPGD. DNA methylation, a crucial biological modification, plays a vital role in gene expression analyses, cellular differentiation, and the study of disease progression. However, accurately and efficiently identifying DNA methylation sites remains a pivotal concern in the field of bioinformatics. The issue addressed in this paper is the presence of methylation in DNA, which is a binary classification problem. To address this, our research aimed to develop a deep learning algorithm capable of more precisely identifying these sites. The DeepPGD framework combined a dual residual structure involving Temporal convolutional networks (TCNs) and bidirectional long short-term memory (BiLSTM) networks to effectively extract intricate DNA structural and sequence features. Additionally, to meet the practical requirements of DNA methylation identification, extensive experiments were conducted across a variety of biological species. The experimental results highlighted DeepPGD’s exceptional performance across multiple evaluation metrics, including accuracy, Matthews’ correlation coefficient (MCC), and the area under the curve (AUC). In comparison to other algorithms in the same domain, DeepPGD demonstrated superior classification and predictive capabilities across various biological species datasets. This significant advancement in algorithmic prowess not only offers substantial technical support, but also holds potential for research and practical implementation within the DNA methylation identification domain. Moreover, the DeepPGD framework shows potential for application in genomics research, biomedicine, and disease diagnostics, among other fields. |
| Keywords | deep learning; RNA methylation; gene expression |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 4018. Nanotechnology |
| Byline Affiliations | Dalian University of Technology, China |
| School of Engineering |
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