Biological evidence has shown that microRNAs(miRNAs) are greatly implicated in various biological progresses involved in human diseases. The identification of miRNA-disease associations(MDAs) is beneficial to disease diagnosis as well as treatment. Due to the high costs of biological experiments, it attracts more and more attention to predict MDAs by computational approaches. In this work, we propose a novel model MTFMDA for miRNA-disease association prediction by matrix tri-factorization, based on the known miRNA-disease associations, two types of miRNA similarities, and two types of disease similarities. The main idea of MTFMDA is to factorize the miRNA-disease association matrix to three matrices, a feature matrix for miRNAs, a feature matrix for diseases, and a low-rank relationship matrix. Our model incorporates the Laplacian regularizers which force the feature matrices to preserve the similarities of miRNAs or diseases. A novel algorithm is proposed to solve the optimization problem. We evaluate our model by 5-fold cross validation by using known MDAs from HMDD V2.0 and show that our model could obtain the significantly highest AUCs among all the state-of-art methods. We further validate our method by applying it on colon and breast neoplasms in two different types of experiment settings. The new identified associated miRNAs for the two diseases could be verified by two other databases including dbDEMC and HMDD V3.0, which further shows the power of our proposed method.

中文翻译：

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通过矩阵三因子预测MicroRNA-疾病关联

生物学证据表明，microRNA（miRNA）与人类疾病涉及的各种生物学进展密切相关。miRNA-疾病关联（MDA）的识别有利于疾病的诊断和治疗。由于生物学实验的高昂成本，通过计算方法预测MDA吸引了越来越多的关注。在这项工作中，我们基于已知的miRNA-疾病关联，两种类型的miRNA相似性和两种类型的疾病相似性，提出了一种通过矩阵三因子预测miRNA-疾病关联的新型MTFMDA模型。MTFMDA的主要思想是将miRNA-疾病关联矩阵分解为三个矩阵，即miRNA的特征矩阵，疾病的特征矩阵和低秩关系矩阵。我们的模型结合了Laplacian正则化器，后者可强制特征矩阵保留miRNA或疾病的相似性。提出了一种新的算法来解决优化问题。我们通过使用来自HMDD V2.0的已知MDA通过5倍交叉验证来评估我们的模型，并表明我们的模型可以在所有现有技术中获得最高的AUC。通过在两种不同类型的实验环境中将其应用于结肠和乳腺肿瘤，我们进一步验证了我们的方法。可以通过两个其他数据库（包括dbDEMC和HMDD V3.0）验证与这两种疾病相关的新鉴定的miRNA，这进一步表明了我们提出的方法的强大功能。我们通过使用来自HMDD V2.0的已知MDA通过5倍交叉验证来评估我们的模型，并表明我们的模型可以在所有现有技术中获得最高的AUC。通过在两种不同类型的实验环境中将其应用于结肠和乳腺肿瘤，我们进一步验证了我们的方法。可以通过两个其他数据库（包括dbDEMC和HMDD V3.0）验证与这两种疾病相关的新鉴定的miRNA，这进一步表明了我们提出的方法的强大功能。我们通过使用来自HMDD V2.0的已知MDA通过5倍交叉验证来评估我们的模型，并表明我们的模型可以在所有现有技术中获得最高的AUC。通过在两种不同类型的实验环境中将其应用于结肠和乳腺肿瘤，我们进一步验证了我们的方法。可以通过两个其他数据库（包括dbDEMC和HMDD V3.0）验证与这两种疾病相关的新鉴定的miRNA，这进一步表明了我们提出的方法的强大功能。