BACKGROUND Studies have shown that miRNAs are functionally associated with the development of many human diseases, but the roles of miRNAs in diseases and their underlying molecular mechanisms have not been fully understood. The research on miRNA-disease interaction has received more and more attention. Compared with the complexity and high cost of biological experiments, computational methods can rapidly and efficiently predict the potential miRNA-disease interaction and can be used as a beneficial supplement to experimental methods. RESULTS In this paper, we proposed a novel computational model of kernel neighborhood similarity and multi-network bidirectional propagation (KNMBP) for miRNA-disease interaction prediction, especially for new miRNAs and new diseases. First, we integrated multiple data sources of diseases and miRNAs, respectively, to construct a novel disease semantic similarity network and miRNA functional similarity network. Secondly, based on the modified miRNA-disease interactions, we use the kernel neighborhood similarity algorithm to calculate the disease kernel neighborhood similarity and the miRNA kernel neighborhood similarity. Finally, we utilize bidirectional propagation algorithm to predict the miRNA-disease interaction scores based on the integrated disease similarity network and miRNA similarity network. As a result, the AUC value of 5-fold cross validation for all interactions by KNMBP is 0.93126 based on the commonly used dataset, and the AUC values for all interactions, for all miRNAs, for all disease is 0.93795、0.86363、0.86937 based on another dataset extracted by ourselves, which are higher than other state-of-the-art methods. In addition, our model has good parameter robustness. The case study further demonstrated the predictive performance of the model for novel miRNA-disease interactions. CONCLUSIONS Our KNMBP algorithm efficiently integrates multiple omics data from miRNAs and diseases to stably and efficiently predict potential miRNA-disease interactions. It is anticipated that KNMBP would be a useful tool in biomedical research.

中文翻译：

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基于核邻域相似性和多网络双向传播的MiRNA疾病相互作用预测。

背景研究表明，miRNA在功能上与许多人类疾病的发展有关，但尚未完全了解miRNA在疾病中的作用及其潜在的分子机制。miRNA-疾病相互作用的研究受到越来越多的关注。与生物学实验的复杂性和高成本相比，计算方法可以快速有效地预测潜在的miRNA-疾病相互作用，并可以用作实验方法的有益补充。结果在本文中，我们提出了一种新的核仁邻域相似性和多网络双向传播（KNMBP）的计算模型，用于miRNA-疾病相互作用的预测，尤其是对于新的miRNA和新疾病的预测。首先，我们分别整合了疾病和miRNA的多个数据源，构建新型的疾病语义相似性网络和miRNA功能相似性网络。其次，基于修饰的miRNA-疾病相互作用，我们使用核邻域相似度算法来计算疾病的核邻域相似度和miRNA核邻域相似度。最后，我们基于整合的疾病相似性网络和miRNA相似性网络，利用双向传播算法来预测miRNA-疾病相互作用分数。结果，基于常用数据集，KNMBP对所有相互作用的5倍交叉验证的AUC值为0.93126，对于所有疾病，对于所有miRNA，对于所有疾病，所有相互作用的AUC值分别为0.93795、0.86363、0.86937我们自己提取的另一个数据集，比其他最新方法要高。此外，我们的模型具有良好的参数鲁棒性。案例研究进一步证明了该模型对新型miRNA-疾病相互作用的预测性能。结论我们的KNMBP算法有效地整合了来自miRNA和疾病的多种组学数据，以稳定有效地预测潜在的miRNA-疾病相互作用。预计KNMBP将成为生物医学研究中的有用工具。