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

被引:38
|
作者
Zhang, Wen [1 ]
Tang, Guifeng [2 ]
Zhou, Shuang [3 ]
Niu, Yanqing [4 ]
机构
[1] Huazhong Agr Univ, Coll Informat, Wuhan 430070, Peoples R China
[2] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China
[3] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Peoples R China
[4] South Cent Univ Nationalities, Sch Math & Stat, Wuhan 430074, Peoples R China
基金
中国国家自然科学基金;
关键词
lncRNA-miRNA interactions; Integrated similarity; Label propagation; LONG NONCODING RNA; CANCER DEVELOPMENT; EXPRESSION; MALAT1; DATABASE; DRUG; PROGRESSION; PROTEINS; DISEASE; SPONGE;
D O I
10.1186/s12864-019-6284-y
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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, SLNPMSC and SLNPM-PC are promising for lncRNA-miRNA interaction prediction.
引用
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页数:12
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