Author:
Fei Yinchao,Zhang Hao,Wang Yili,Liu Zhen,Liu Yuanning
Abstract
Abstract
Background
RNA secondary structure is very important for deciphering cell’s activity and disease occurrence. The first method which was used by the academics to predict this structure is biological experiment, But this method is too expensive, causing the promotion to be affected. Then, computing methods emerged, which has good efficiency and low cost. However, the accuracy of computing methods are not satisfactory. Many machine learning methods have also been applied to this area, but the accuracy has not improved significantly. Deep learning has matured and achieves great success in many areas such as computer vision and natural language processing. It uses neural network which is a kind of structure that has good functionality and versatility, but its effect is highly correlated with the quantity and quality of the data. At present, there is no model with high accuracy, low data dependence and high convenience in predicting RNA secondary structure.
Results
This paper designs a neural network called LTPConstraint to predict RNA secondary structure. The network is based on many network structure such as Bidirectional LSTM, Transformer and generator. It also uses transfer learning to train modelso that the data dependence can be reduced.
Conclusions
LTPConstraint has achieved high accuracy in RNA secondary structure prediction. Compared with the previous methods, the accuracy improves obviously both in predicting the structure with pseudoknot and the structure without pseudoknot. At the same time, LTPConstraint is easy to operate and can achieve result very quickly.
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
National Key Research and Development Project of China
Jilin Province Industrial Innovation Special Fund Project
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Reference58 articles.
1. Cooper TA, Wan L, Dreyfuss G. RNA and disease. Cell. 2012;136(4):777–93.
2. Wang J, Jie Z, Li K, Zhao W, Cui Q. SpliceDisease database: linking RNA splicing and disease. Nucleic Acids Res. 2012;40(D1):1055–9.
3. Sloma MF, Mathews DH, Chen SJ. Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs. PLOS Comput Biol. 2017;13:e1005827.
4. Pleij Wa C. RNA pseudoknot: structure, detection, and prediction. Methods Enzymol. 1989;180:289–303.
5. Chowdhury L, Khan MI. Pseudoknots prediction on RNA secondary structure using term rewriting. In: International conference on bioinformatics & biomedical engineering
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