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BMC Genomics. 2019 Dec 20;20(Suppl 11):946. doi: 10.1186/s12864-019-6284-y.

LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination.

Author information

1
College of informatics, Huazhong Agricultural University, Wuhan, 430070, China. zhangwen@mail.hzau.edu.cn.
2
School of Computer Science, Wuhan University, Wuhan, 430072, China.
3
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
4
School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan, 430074, China. niuyanqing@mail.scuec.edu.cn.

Abstract

BACKGROUND:

Researchers discover lncRNAs can act as decoys or sponges to regulate the behavior of miRNAs. Identification of lncRNA-miRNA interactions helps to understand the functions of lncRNAs, especially their roles in complicated diseases. Computational methods can save time and reduce cost in identifying lncRNA-miRNA interactions, but there have been only a few computational methods.

RESULTS:

In this paper, we propose a sequence-derived linear neighborhood propagation method (SLNPM) to predict lncRNA-miRNA interactions. First, we calculate the integrated lncRNA-lncRNA similarity and the integrated miRNA-miRNA similarity by combining known lncRNA-miRNA interactions, lncRNA sequences and miRNA sequences. We consider two similarity calculation strategies respectively, namely similarity-based information combination (SC) and interaction profile-based information combination (PC). Second, the integrated lncRNA similarity-based graph and the integrated miRNA similarity-based graph are respectively constructed, and the label propagation processes are implemented on two graphs to score lncRNA-miRNA pairs. Finally, the weighted averages of their outputs are adopted as final predictions. Therefore, we construct two editions of SLNPM: sequence-derived linear neighborhood propagation method based on similarity information combination (SLNPM-SC) and sequence-derived linear neighborhood propagation method based on interaction profile information combination (SLNPM-PC). The experimental results show that SLNPM-SC and SLNPM-PC predict lncRNA-miRNA interactions with higher accuracy compared with other state-of-the-art methods. The case studies demonstrate that SLNPM-SC and SLNPM-PC help to find novel lncRNA-miRNA interactions for given lncRNAs or miRNAs.

CONCLUSION:

The study reveals that known interactions bring the most important information for lncRNA-miRNA interaction prediction, and sequences of lncRNAs (miRNAs) also provide useful information. In conclusion, SLNPM-SC and SLNPM-PC are promising for lncRNA-miRNA interaction prediction.

KEYWORDS:

Integrated similarity; Label propagation; lncRNA-miRNA interactions

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