Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 35, 26 April 2024
Open
Access | Article
Optimization method of protein coding region identification based on IHHO-CNN-LSTM
* Author to whom correspondence should be addressed.
Abstract
Aiming at the current problem of insufficient identification accuracy of coding regions in DNA sequences, this study proposes a protein coding region identification method based on IHHO-CNN-LSTM. Firstly, the data preprocessing of DNA sequences is transformed into feature vectors, and then the protein coding region identification model based on CNN-LSTM is established. To address the limitations of parameter selection of CNN-LSTM, a hybrid strategy improved Harris Hawk Optimization (HHO) algorithm is introduced to achieve adaptive parameter searching of CNN-LSTM, so as to obtain the optimization model of white matter coding region identification based on IHHO-CNN-LSTM. The improved model was used to accurately distinguish coding and non-coding regions. Two benchmark datasets, HMR195 and BG570, are selected for five-fold cross-validation, and the results show that the AUC values of the model designed in this paper are 0.9854 and 0.9895, the corresponding identification accuracy is 0.9527 and 0.9645, respectively, which are significantly better than other models, and also have a significant advantage in terms of computational efficiency. The proposed method can efficiently and accurately identify protein coding regions, which can help promote the related research in the field of genetic engineering.
Keywords
protein coding region identification; CNN-LSTM; Harris Hawk Optimization algorithm; hybrid strategy
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Modern Medicine and Global Health
- ISBN (Print)
- 978-1-83558-395-1
- ISBN (Online)
- 978-1-83558-396-8
- Published Date
- 26 April 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/35/20240862
- Copyright
- 26 April 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited