MicroRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a method, collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations. The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix. Then the Weight K Nearest Known Neighbors method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the fivefold cross-validation, with an AUC of 0.9569 (0.0005). The AUC value of MCCMF is higher than other advanced methods in the fivefold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

中文翻译：

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MCCMF：基于矩阵完成度的协作矩阵分解，用于预测miRNA-疾病关联

微小RNA（miRNA）是具有调节功能的非编码RNA。许多研究表明，miRNA与人类疾病密切相关。在探索miRNA与疾病之间关系的方法中，传统方法耗时且需要提高准确性。鉴于先前模型的不足，提出了一种基于矩阵完成（MCCMF）的协作矩阵分解方法来预测未知的miRNA-疾病关联。miRNA和疾病的完整基质是通过基质完成获得的。此外，将高斯交互作用特征核添加到miRNA功能相似性矩阵和疾病语义相似性矩阵。然后使用权重K最近邻法对关联矩阵进行预处理，因此模型接近实际。最后，应用协同矩阵分解方法获得预测结果。因此，MCCMF在五重交叉验证中获得了令人满意的结果，AUC为0.9569（0.0005）。在五重交叉验证实验中，MCCMF的AUC值高于其他先进方法。为了全面评估MCCMF的性能，还添加了准确性，精度，召回率和f量度。最终的实验结果表明，MCCMF在预测miRNA-疾病关联方面优于其他方法。最后，通过研究三种特定疾病进一步验证了MCCMF的有效性和实用性。9569（0.0005）。在五重交叉验证实验中，MCCMF的AUC值高于其他先进方法。为了全面评估MCCMF的性能，还添加了准确性，精度，召回率和f量度。最终的实验结果表明，MCCMF在预测miRNA-疾病关联方面优于其他方法。最后，通过研究三种特定疾病进一步验证了MCCMF的有效性和实用性。9569（0.0005）。在五重交叉验证实验中，MCCMF的AUC值高于其他先进方法。为了全面评估MCCMF的性能，还添加了准确性，精度，召回率和f量度。最终的实验结果表明，MCCMF在预测miRNA-疾病关联方面优于其他方法。最后，通过研究三种特定疾病进一步验证了MCCMF的有效性和实用性。