Predicting the associations between microRNAs (miRNAs) and diseases is of great significance for identifying miRNAs related to human diseases. Since it is time-consuming and costly to identify the association between miRNA and disease through biological experiments, computational methods are currently used as an effective supplement to identify the potential association between disease and miRNA. This paper presents a Multi-view Kernel Fusion Network (MvKFN) based prediction method (MvKFN-MDA) to address the problem of miRNA-disease associations prediction. A novel multiple kernel fusion framework Multi-view Kernel Fusion Network (MvKFN) is first proposed to effectively fuse different views similarity kernels constructed from different data sources in a highly nonlinear way. Using MvKFNs, both different base similarity kernels for miRNA, such as sequence, functional, semantic, Gaussian profile kernels and different base similarity kernels for diseases, such as semantic, Gaussian profile kernel are nonlinearly fused into two integrated similarity kernels, one for miRNA, another for disease. Then, miRNA and disease feature representations are extracted from the miRNA and disease integrated similarity kernels respectively. These features are then fed into a neural matrix completion framework which finally outputs the association prediction scores. The parameters of MvKFN-MDA are learned based on the known miRNA-disease association matrix in a supervised end-to-end way. We compare the proposed method with other state-of-the-art methods. The AUCs of our proposed method were superior to the existing methods in both 5-FCV and LOOCV on two open experimental datasets. Furthermore, 49, 48, and 47 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma, and kidney cancer, are verified respectively using experimental literature. Finally, 100% accuracy from the top 50 predicted miRNAs is achieved when breast cancer is used as a case study to evaluate the ability of MvKFN-MDA for predicting a new disease without any known related miRNAs.

预测 microRNAs (miRNAs) 与疾病之间的关联对于识别与人类疾病相关的 miRNAs 具有重要意义。由于通过生物学实验来识别miRNA与疾病之间的关联既费时又费钱，因此计算方法目前被用作识别疾病与miRNA之间潜在关联的有效补充。本文提出了一种基于多视图内核融合网络 (MvKFN) 的预测方法 (MvKFN-MDA) 来解决 miRNA 与疾病关联预测的问题。首次提出了一种新颖的多核融合框架多视图内核融合网络（MvKFN），以高度非线性的方式有效地融合从不同数据源构建的不同视图相似性内核。使用 MvKFN，两种不同的 miRNA 碱基相似性内核，例如序列、功能、语义、高斯轮廓核和疾病的不同碱基相似性核，例如语义，高斯轮廓核非线性地融合成两个集成的相似性核，一个用于 miRNA，另一个用于疾病。然后，分别从 miRNA 和疾病综合相似核中提取 miRNA 和疾病特征表示。然后将这些特征输入到神经矩阵完成框架中，最终输出关联预测分数。MvKFN-MDA 的参数是基于已知的 miRNA-疾病关联矩阵以有监督的端到端方式学习的。我们将提出的方法与其他最先进的方法进行比较。在两个开放的实验数据集上，我们提出的方法的 AUC 优于 5-FCV 和 LOOCV 中的现有方法。此外，49、48、并利用实验文献分别验证了结肠癌、淋巴瘤和肾癌三种高危人类疾病的前50个预测miRNA中的47个。最后，当使用乳腺癌作为案例研究来评估 MvKFN-MDA 在没有任何已知相关 miRNA 的情况下预测新疾病的能力时，前 50 个预测 miRNA 的准确率达到 100%。