ArticleBMC bioinformatics2021
A novel end-to-end method to predict RNA secondary structure profile based on bidirectional LSTM and residual neural network.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 21 citations in OpenAlex.
- Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes.Genomics, proteomics & bioinformatics · 2024Article
- RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction.Journal of molecular biology · 2024Article
- Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.Current drug targets · 2024Review
- Article
- Deep learning models for RNA secondary structure prediction (probably) do not generalize across families.Bioinformatics (Oxford, England) · 2022Article
- Res-Dom: predicting protein domain boundary from sequence using deep residual network and Bi-LSTM.Bioinformatics advances · 2022Article
- CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network.BMC bioinformatics · 2021Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundStudies have shown that RNA secondary structure, a planar structure formed by paired bases, plays diverse vital roles in fundamental life activities and complex diseases. RNA secondary structure profile can record whether each base is paired with others. Hence, accurate prediction of secondary structure profile can help to deduce the secondary structure and binding site of RNA. RNA secondary structure profile can be obtained through biological experiment and calculation methods. Of them, the biological experiment method involves two ways: chemical reagent and biological crystallization. The chemical reagent method can obtain a large number of prediction data, but its cost is high and always associated with high noise, making it difficult to get results of all bases on RNA due to the limited of sequencing coverage. By contrast, the biological crystallization method can lead to accurate results, yet heavy experimental work and high costs are required. On the other hand, the calculation method is CROSS, which comprises a three-layer fully connected neural network. However, CROSS can not completely learn the features of RNA secondary structure profile since its poor network structure, leading to its low performance.
resultsIn this paper, a novel end-to-end method, named as "RPRes, was proposed to predict RNA secondary structure profile based on Bidirectional LSTM and Residual Neural Network.
conclusionsRPRes utilizes data sets generated by multiple biological experiment methods as the training, validation, and test sets to predict profile, which can compatible with numerous prediction requirements. Compared with the biological experiment method, RPRes has reduced the costs and improved the prediction efficiency. Compared with the state-of-the-art calculation method CROSS, RPRes has significantly improved performance.
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