Plant MiRNA Target Prediction Research Based On Deep Learning

There are various types and functions of RNA in living organisms.One type of noncoding RNA is a newly discovered type of RNA in recent years.This type of RNA mainly includes microRNA(miRNA),piRNA etc.,they mainly play a regulatory role in the life process of organisms,and miRNA is the most representative type of non-coding RNA.In plants,miRNAs can recognize each other with target genes by means of complementary base matching,and use this to mediate target gene-mediated translation inhibition or cut the target gene,thereby affecting the expression of gene traits.Based on the important role of miRNA and its targeting mechanism on organisms,this paper studies the biological characteristics of plant miRNAs and target genes,and uses deep learning algorithms to design a plant miRNA target gene prediction model:DeepMiRNA,and develops a webbased Plant miRNA target gene prediction system.Since the discovery of miRNA,the amount of miRNA-related data has been rising.The prediction of miRNA target genes has also changed from the traditional single target gene sequence verification to the use of computer technology big data and machine learning,deep learning and other technologies for prediction.Because a miRNA generally has multiple target genes,the generation of calculation methods has greatly promoted the efficiency and accuracy of prediction of miRNA target genes.Therefore,based on the current research status,this paper uses convolutional neural network(CNN)and the special form of recurrent neural network(BiLSTM),which has excellent performance in sequencebased natural language processing,to design a deep miRNA prediction model for plant miRNA target genes.In terms of data selection,this paper selects miRNA data from three plants of Arabidopsis thaliana,soybean,rice and mixed the three types of plant data to generate mixed data.The processing of the data includes processes such as base replacement,sequence completion,and data encoding of the original genetic data,so as to convert the original genetic data into a data structure that can be input to a model.After model training and testing experiments,the results show that the DeepMiRNA model can achieve an accuracy rate of about 93% in Arabidopsis thaliana-based data;an accuracy rate of about 88% in soybean-based data;and in rice-based data it can reach an accuracy rate of about 91%;it can reach an accuracy rate of about 90% in mixed-based data.Aftercomparison with other classification algorithms,it shows that the DeepMiRNA model performs well in predicting plant miRNA target genes,and the prediction structure is superior to other algorithms in comparison,indicating that this model can achieve better classification of this problem.In order to further promote the application of the DeepMiRNA model in plant miRNA target gene prediction,a plant miRNA target gene prediction system has been developed in this paper.Users can use this system to perform online target gene prediction and obtain prediction results(http://www.deepbiology.cn/deepmi/).