Prediction of miRNA targets by learning from interaction sequences

Xueming Zheng; Long Chen; Xiuming Li; Ying Zhang; Shungao Xu; Xinxiang Huang

doi:10.1371/journal.pone.0232578

Prediction of miRNA targets by learning from interaction sequences

PLoS One. 2020 May 5;15(5):e0232578. doi: 10.1371/journal.pone.0232578. eCollection 2020.

Authors

Xueming Zheng^{1

2}, Long Chen², Xiuming Li³, Ying Zhang¹, Shungao Xu¹, Xinxiang Huang¹

Affiliations

¹ Department of Biochemistry and Molecular Biology, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, P. R. China.
² Department of Clinical Laboratory, the First People's Hospital of Zhangjiagang, Zhangjiagang, Jiangsu, P. R. China.
³ School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, P. R. China.

Abstract

MicroRNAs (miRNAs) are involved in a diverse variety of biological processes through regulating the expression of target genes in the post-transcriptional level. So, it is of great importance to discover the targets of miRNAs in biological research. But, due to the short length of miRNAs and limited sequence complementarity to their gene targets in animals, it is challenging to develop algorithms to predict the targets of miRNA accurately. Here we developed a new miRNA target prediction algorithm using a multilayer convolutional neural network. Our model learned automatically the interaction patterns of the experiment-validated miRNA:target-site chimeras from the raw sequence, avoiding hand-craft selection of features by domain experts. The performance on test dataset is inspiring, indicating great generalization ability of our model. Moreover, considering the stability of miRNA:target-site duplexes, our method also showed good performance to predict the target transcripts of miRNAs.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Animals
Computer Simulation
Gene Expression Regulation*
Humans
MicroRNAs / genetics*
Models, Genetic
Neural Networks, Computer*
Sequence Analysis, RNA

Substances

MicroRNAs

Grants and funding

This work was supported by China Postdoctoral Science Foundation Funded Project (2018M642186) and National Natural Science Foundation of China (31670131). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.