基于深度学习的核糖核酸二级结构预测方法

doi:10.13229/j.cnki.jdxbgxb.20230267

Abstract

Abstract:

A new method based on deep learning， UCEfold， is proposed for predicting ribonucleic acid （RNA） secondary structure using both “sequence” and “image” as input to the deep learning model to extract hidden features. It also added some prior knowledge to the model to improve the prediction accuracy. There have tested the UCEfold model on both RNAStralign and ArchiveⅡ datasets， and the results show that UCEfold outperforms the traditional method significantly， and can predict the RNA sequences with pseudoknots more accurately and has strong generalization ability， which effectively solves the bottleneck of the traditional algorithm with high complexity， low efficiency and inability to predict pseudoknots.

Key words: computer application, deep learning, RNA secondary structure prediction, pseudoknots, attentional mechanisms

CLC Number:

TP399

Yuan-ning LIU,Zi-nan ZANG,Hao ZHANG,Zhen LIU. Deep learning-based method for ribonucleic acid secondary structure prediction[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(1): 297-306.

Figures/Tables 13

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方法	F₁分数	Prec	Rec
UCEfold	0.982	0.984	0.981
E2Efold	0.842	0.872	0.824
RNAfold	0.583	0.557	0.614
Linearfold	0.653	0.660	0.657
RNAstructure	0.571	0.550	0.597
Contrafold	0.648	0.619	0.684
Mfold	0.556	0.541	0.574

方法	F₁分数	Prec	Rec
UCEfold	0.919	0.937	0.910
E2Efold	0.554	0.606	0.531
RNAfold	0.579	0.553	0.614
Linearfold	0.608	0.630	0.607
RNAstructure	0.577	0.554	0.607
Contrafold	0.619	0.595	0.652
Mfold	0.569	0.553	0.591

方法	grp1	16S_rRNA	RNaseP
UCEfold	0.986	0.990	0.972
E2Efold	0.336	0.636	0.211
RNAfold	0.442	0.471	0.256
Linearfold	0.421	0.517	0.246
RNAstructure	0.436	0.480	0.260
Contrafold	0.447	0.568	0.298
Mfold	0.399	0.505	0.258

方法	F₁分数	Prec	Rec
去除“序列”输入	0.942	0.927	0.959
去除“图像”输入	0.879	0.885	0.876
去除先验知识	0.943	0.930	0.960
无任何去除	0.982	0.984	0.981

Deep learning-based method for ribonucleic acid secondary structure prediction

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