A large number of biological studies have shown that microRNAs (miRNAs) are closely related to the occurrence and development of various human diseases. Nowadays, more and more research has explored the relationship between miRNAs and human diseases. However, existing known associations are often sparse, and it is not easy to predict the potential miRNA-disease associations accurately from large amounts of biological data. Hence, how to predict these associations effectively is an exploratory scientific topic. In this work, we propose a new matrix completion algorithm based on non-negative matrix factorization (NMFMC) to infer potential miRNA-disease associations. In NMFMC, we decompose the miRNA-disease association matrix into a known part and an unknown part. In such a manner, the experimentally validated associations can be well preserved, and the potential associations can be better recovered. In addition, both disease similarity and miRNA similarity are embedded into the proposed model to assist the association recovering process. As a result, the non-negative matrix factorization, matrix completion and graph regularization constraints are integrated into a unified framework to serve miRNA-disease association prediction. The validity of our method is confirmed by global and local leave-one-out-cross-validation and achieves AUCs of 0.9165 and 0.8512, respectively, which is an effective improvement over previous methods. Furthermore, we conduct case studies on three widespread human diseases, and NMFMC is also applicable. For Colon Neoplasms, Prostate Neoplasms, and Breast Neoplasms, 45, 44, and 50 of the top 50 predictions based on existing associations are confirmed by experimental reports.

大量生物学研究表明，微小RNA（miRNA）与人类各种疾病的发生发展密切相关。如今，越来越多的研究探索了miRNA与人类疾病之间的关系。然而，现有的已知关联往往稀少，从大量生物学数据中准确预测潜在的miRNA-疾病关联并不容易。因此，如何有效地预测这些关联是一个探索性的科学课题。在这项工作中，我们提出了一种基于非负矩阵分解 (NMFMC) 的新矩阵完成算法来推断潜在的 miRNA-疾病关联。在 NMFMC 中，我们将 miRNA-疾病关联矩阵分解为已知部分和未知部分。通过这种方式，可以很好地保留经过实验验证的关联，并且可以更好地恢复潜在的关联。此外，疾病相似性和 miRNA 相似性都嵌入到所提出的模型中，以协助关联恢复过程。因此，非负矩阵分解、矩阵完成和图正则化约束被集成到一个统一的框架中，以服务于 miRNA-疾病关联预测。我们方法的有效性得到了全局和局部留一法交叉验证的证实，AUC 分别为 0.9165 和 0.8512，这是对以前方法的有效改进。此外，我们对三种普遍存在的人类疾病进行案例研究，NMFMC 也适用。对于结肠肿瘤、前列腺肿瘤和乳腺肿瘤，45, 44,