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Math Biosci. 2019 Jun;312:67-76. doi: 10.1016/j.mbs.2019.04.006. Epub 2019 Apr 26.

Predicting miRNA-lncRNA interactions and recognizing their regulatory roles in stress response of plants.

Author information

1
School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China. Electronic address: ismalia@mail.dlut.edu.cn.
2
School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China. Electronic address: kangqiang@mail.dlut.edu.cn.
3
School of Life Bioengineering, Dalian University of Technology, Dalian, Liaoning, 116023, China. Electronic address: luanyush@dlut.edu.cn.
4
School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China. Electronic address: mengjun@dlut.edu.cn.

Abstract

It has been found that each non-coding RNA (ncRNA) can act not only through its target gene, but also interact with each other to act on biological traits, and this interaction is more common. Many studies focus mainly on the analysis of microRNA(miRNA) and message RNA (mRNA) interactions. In this study, we investigated miRNA and long non-coding RNA (lncRNA) interactions using support vector regression (SVR) for prediction of new target genes in Arabidopsis thaliana and identify some regulatory roles in stress response. The networks of miRNA-mRNA, miRNA-lncRNA and miRNA-mRNA-lncRNA were constructed. They were further analyzed and interpreted in R. We showed that miRNA with low sequence number, targeted lncRNA with high sequence number and miRNA with high sequence number targeted lncRNA with low sequence number. The experimental results showed that there is a regulatory relationship between miRNA-lncRNA. New RNA targets were predicted using SVR with new gene expression mechanism and the stress related functions were annotated.

KEYWORDS:

Prediction; Regulatory network; Support vector regression; miRNA-lncRNA interaction

PMID:
31034845
DOI:
10.1016/j.mbs.2019.04.006

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