摘要
MicroRNAs(miRNAs)是一类长约22~23碱基(nt)的单链非编码RNA,在生物进化方面有着重要意义。成熟的miRNA会通过其种子序列(5’第2-8位核苷酸)与message RNAs(mRNAs)的3’UTR区域靶位点进行完全或不完全配对,实现切割mRNA及抑制mRNA翻译等功能。由于miRNA结合mRNA靶位点的机制仍未明确,因此预测miRNA靶位点的工作一直是miRNA研究领域的一大挑战和难题。实验方法虽然准确,但耗时长且昂贵。在生物信息领域,基于规则匹配的常规计算方法虽然能进行靶位点的预测,但存在着准确率偏低的问题。随着深度学习的兴起及实验验证数据及具体靶位点信息的丰富,基于深度学习的方法成为了miRNA靶位点预测领域的研究热点。首先介绍了常用的miRNA预测数据集、预测类型和常见特征;之后对预测研究中常用的深度学习模型进行阐述;接着介绍了常规的预测方法及基于深度学习的预测方法,并对这些方法进行了分类总结和性能的对比分析;最后对使用深度学习的预测工作当前存在的问题及未来的发展进行了探讨。
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.
作者
李亚男
胡宇佳
甘伟
朱敏
LI Ya-nan;HU Yu-jia;GAN Wei;ZHU Min(College of Computer Science,Sichuan University,Chengdu 610065,China)
出处
《计算机科学》
CSCD
北大核心
2021年第1期209-216,共8页
Computer Science
基金
“十三五”国家科技重大专项(2018ZX10201002)。
