LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination
LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination
复制标题
通过序列衍生的线性邻域传播方法和信息组合预测 LncRNA-miRNA 相互作用
DOI：
10.1186/s12864-019-6284-y
复制
发表时间：
2019-12-20
期刊：
影响因子：
4.4
通讯作者：
Niu, Yanqing
中科院分区：
文献类型：
--
作者：
Zhang, Wen;Tang, Guifeng;Niu, Yanqing
研究背景lncRNA可以作为诱饵或海绵来调节miRNAs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，降低成本，在确定lncRNA-miRNA的相互作用，但只有少数的计算methods.ResultsIn本文中，我们提出了一个序列衍生的线性邻域传播方法（SLNPM）预测lncRNA-miRNA的相互作用。首先，我们通过结合已知的lncRNA-miRNA相互作用、lncRNA序列和miRNA序列来计算整合的lncRNA-lncRNA相似性和整合的miRNA-miRNA相似性。我们分别考虑了两种相似度计算策略，即基于相似度的信息组合（SC）和基于交互轮廓的信息组合（PC）。其次，分别构建基于lncRNA相似性的整合图和基于miRNA相似性的整合图，并在两个图上实现标签传播过程，对lncRNA-miRNA对进行评分。最后，将其输出的加权平均值作为最终预测值。因此，我们构造了两种版本的SLNPM：基于相似性信息组合的序列推导线性邻域传播方法（SLNPM-SC）和基于相互作用轮廓信息组合的序列推导线性邻域传播方法（SLNPM-PC）。实验结果表明，SLNPM-SC和SLNPM-PC预测lncRNA-miRNA相互作用的准确性比其他国家的最先进的方法。结果表明，SLNPM-SC和SLNPM-PC有助于发现新的lncRNA-miRNA相互作用。结论已知的相互作用为预测lncRNA-miRNA相互作用提供了最重要的信息，lncRNAs（miRNAs）序列也提供了有用的信息。总之，SLNPM-SC和SLNPM-PC在预测lncRNA-miRNA相互作用中具有良好的应用前景。
BackgroundResearchers 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.ResultsIn 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.ConclusionThe 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.