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

机译:LNCRNA-MiRNA通过信息组合通过序列推导的线性邻域传播方法预测

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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.
机译:背景:研究人员发现LNCRNA可以作为诱饵或海绵来调节miRNA的行为。 LNCRNA-miRNA相互作用的鉴定有助于了解LNCRNA的功能,特别是它们在复杂疾病中的作用。计算方法可以节省时间并降低识别LNCRNA-miRNA交互的成本,但是只有几个计算方法。结果:在本文中,我们提出了一种序列衍生的线性邻域传播方法(SLNPM)以预测LNCRNA-MiRNA相互作用。首先,通过结合已知的LNCRNA-miRNA相互作用,LNCRNA序列和miRNA序列来计算集成的LNCRNA-LNCRNA相似性和集成的miRNA-miRNA相似度。我们考虑了两个相似度计算策略,即基于相似性的信息组合(SC)和基于交互配置文件的信息组合(PC)。其次,分别构造了基于LNCRNA相似性的基于综合的基于MiRNA相似性的图表,并在两个图中实现了标签传播过程以得分LNCRNA-miRNA对。最后,将其输出的加权平均值作为最终预测。因此,我们构建了两个SLNPM:基于相似性信息组合(SLNPM-SC)和序列导出的基于交互信息组合(SLNPM-PC)的序列推导的线性邻域传播方法的序列推导的线性邻域传播方法。实验结果表明,与其他最先进的方法相比,SLNPM-SC和SLNPM-PC预测LNCRNA-MiRNA与更高精度相互作用。案例研究表明,SLNPM-SC和SLNPM-PC有助于找到给定LNCRNA或miRNA的新型LNCRNA-miRNA相互作用。结论:该研究表明,已知的相互作用为LNCRNA-miRNA相互作用预测和LNCRNA的序列带来了最重要的信息(miRNA)还提供有用的信息。总之,SLNPM-SC和SLNPM-PC对LNCRNA-miRNA相互作用预测有望。

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