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摘要:
Noncoding RNAs play important roles in cell and their secondary structures are vital for understanding their tertiary structures and functions.Many prediction methods of RNA secondary structures have been proposed but it is still challenging to reach high accuracy,especially for those with pseudoknots.Here we present a coupled deep learning model,called 2dRNA,to predict RNA secondary structure.It combines two famous neural network architectures bidirectional LSTM and U-net and only needs the sequence of a target RNA as input.Benchmark shows that our method can achieve state-of-the-art performance compared to current methods on a testing dataset.Our analysis also shows that 2dRNA can learn structural information from similar RNA sequences without aligning them.
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篇名 Prediction of RNA secondary structure with pseudoknots using coupled deep neural networks
来源期刊 生物物理学报:英文版 学科 生物学
关键词 RNA secondary structure prediction Deep learning Minimum free energy
年,卷(期) 2020,(4) 所属期刊栏目
研究方向 页码范围 146-154
页数 9页 分类号 Q52
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RNA
secondary
structure
prediction
Deep
learning
Minimum
free
energy
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研究去脉
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生物物理学报:英文版
双月刊
2364-3439
10-1302/Q
Institute of Biophys
出版文献量(篇)
32
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