Computer Science ›› 2021, Vol. 48 ›› Issue (1): 209-216.doi: 10.11896/jsjkx.191200111

• Artificial Intelligence • Previous Articles     Next Articles

Survey on Target Site Prediction of Human miRNA Based on Deep Learning

LI Ya-nan, HU Yu-jia, GAN Wei, ZHU Min   

  1. College of Computer Science,Sichuan University,Chengdu 610065,China
  • Received:2019-12-18 Revised:2020-05-11 Online:2021-01-15 Published:2021-01-15
  • About author:LI Ya-nan,born in 1995,postgraduate,is a member of China Computer Federation.His main research interests include data mining and bioinformatics.ZHU Min,born in 1971,Ph.D,professor,is a senior member of China Computer Federation.Her main research interests include bioinformatics,information visualization and visual analytics.
  • Supported by:
    National Science and Technology Major Project During the Thirteenth Five-Year Plan(2018ZX10201002).

Abstract: MicroRNAs(miRNAs) are 22~23 nt small non-coding RNAs that play an important role in biological evolution.Mature miRNA will completely or incompletely pair with the target site in 3'UTR region of message RNAs(mRNAs) through its seed region,to achieve the function of cleavage and translational repression so on.As the mechanism of miRNA binding to mRNA target sites is still unclear,the prediction of miRNA target sites has been a major challenge and problem in the field of miRNA research.Although the experimental method is accurate,it is time-consuming and expensive.In Bioinformatics,although the calculation method based on rule matching can predict the target site,it has the problem of low accuracy.With the development of deep learning and the abundance of experimental data,the method based on deep learning has become a research hotspot in the field of miRNA target prediction.Firstly,this paper introduces the commonly used data sets,prediction types and common feature of miRNA prediction,then explains the commonly used deep learning model in prediction research.Next,the conventional prediction methods and prediction methods based on deep learning are introduced.Meanwhile,these methods are classified and summarized.Finally,the current problems and future development of using deep learning to predict miRNA target are discussed.

Key words: Autoencoder, Convolutional neural network, miRNA, Recurrent neural network, Target site prediction

CLC Number: 

  • TP181
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