ArticleBMC bioinformatics2021
Research on RNA secondary structure predicting via bidirectional recurrent 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 6 papers.
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Who cites it
6 citing papers in PubMed.
- Transformers in RNA structure prediction: A review.Computational and structural biotechnology journal · 2025Review
- A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification.PloS one · 2025Article
- Molecular insights into regulatory RNAs in the cellular machinery.Experimental & molecular medicine · 2024Review
- Deciphering phenotyping, DNA barcoding, and RNA secondary structure predictions in eggplant wild relatives provide insights for their future breeding strategies.Scientific reports · 2023Article
- Machine learning for RNA 2D structure prediction benchmarked on experimental data.Briefings in bioinformatics · 2023Review
- Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks.Frontiers in genetics · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundRNA secondary structure prediction is an important research content in the field of biological information. Predicting RNA secondary structure with pseudoknots has been proved to be an NP-hard problem. Traditional machine learning methods can not effectively apply protein sequence information with different sequence lengths to the prediction process due to the constraint of the self model when predicting the RNA secondary structure. In addition, there is a large difference between the number of paired bases and the number of unpaired bases in the RNA sequences, which means the problem of positive and negative sample imbalance is easy to make the model fall into a local optimum. To solve the above problems, this paper proposes a variable-length dynamic bidirectional Gated Recurrent Unit(VLDB GRU) model. The model can accept sequences with different lengths through the introduction of flag vector. The model can also make full use of the base information before and after the predicted base and can avoid losing part of the information due to truncation. Introducing a weight vector to predict the RNA training set by dynamically adjusting each base loss function solves the problem of balanced sample imbalance.
resultsThe algorithm proposed in this paper is compared with the existing algorithms on five representative subsets of the data set RNA STRAND. The experimental results show that the accuracy and Matthews correlation coefficient of the method are improved by 4.7% and 11.4%, respectively.
conclusionsThe flag vector introduced allows the model to effectively use the information before and after the protein sequence; the introduced weight vector solves the problem of unbalanced sample balance. Compared with other algorithms, the LVDB GRU algorithm proposed in this paper has the best detection results.
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