Cai Lijun, Gao Mingyu, Ren Xuanbai, Fu Xiangzheng, Xu Junlin, Wang Peng, Chen Yifan
College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
Front Plant Sci. 2022 Mar 25;13:861886. doi: 10.3389/fpls.2022.861886. eCollection 2022.
Knowledge of the interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) is the basis of understanding various biological activities and designing new drugs. Previous computational methods for predicting lncRNA-miRNA interactions lacked for plants, and they suffer from various limitations that affect the prediction accuracy and their applicability. Research on plant lncRNA-miRNA interactions is still in its infancy. In this paper, we propose an accurate predictor, MILNP, for predicting plant lncRNA-miRNA interactions based on improved linear neighborhood similarity measurement and linear neighborhood propagation algorithm. Specifically, we propose a novel similarity measure based on linear neighborhood similarity from multiple similarity profiles of lncRNAs and miRNAs and derive more precise neighborhood ranges so as to escape the limits of the existing methods. We then simultaneously update the lncRNA-miRNA interactions predicted from both similarity matrices based on label propagation. We comprehensively evaluate MILNP on the latest plant lncRNA-miRNA interaction benchmark datasets. The results demonstrate the superior performance of MILNP than the most up-to-date methods. What's more, MILNP can be leveraged for isolated plant lncRNAs (or miRNAs). Case studies suggest that MILNP can identify novel plant lncRNA-miRNA interactions, which are confirmed by classical tools. The implementation is available on https://github.com/HerSwain/gra/tree/MILNP.
了解长链非编码RNA（lncRNA）与微小RNA（miRNA）之间的相互作用是理解各种生物活性和设计新药的基础。以前用于预测lncRNA-miRNA相互作用的计算方法在植物领域较为缺乏，并且存在各种局限性，影响了预测准确性及其适用性。植物lncRNA-miRNA相互作用的研究仍处于起步阶段。在本文中，我们提出了一种精确的预测器MILNP，用于基于改进的线性邻域相似性测量和线性邻域传播算法预测植物lncRNA-miRNA相互作用。具体而言，我们基于lncRNA和miRNA的多个相似性概况提出了一种基于线性邻域相似性的新型相似性度量，并得出更精确的邻域范围，以摆脱现有方法的局限性。然后，我们基于标签传播同时更新从两个相似性矩阵预测的lncRNA-miRNA相互作用。我们在最新的植物lncRNA-miRNA相互作用基准数据集上对MILNP进行了全面评估。结果表明，MILNP的性能优于最新方法。此外，MILNP可用于单独的植物lncRNA（或miRNA）。案例研究表明，MILNP可以识别新的植物lncRNA-miRNA相互作用，这些相互作用已得到经典工具的证实。该实现可在https://github.com/HerSwain/gra/tree/MILNP上获取。
