MicroRNAs (miRNAs) are a class of non-coding RNAs that play critical role in many biological processes, such as cell growth, development, differentiation and aging. Increasing studies have revealed that miRNAs are closely involved in many human diseases. Therefore, the prediction of miRNA-disease associations is of great significance to the study of the pathogenesis, diagnosis and intervention of human disease. However, biological experimentally methods are usually expensive in time and money, while computational methods can provide an efficient way to infer the underlying disease-related miRNAs. In this study, we propose a novel method to predict potential miRNA-disease associations, called SVAEMDA. Our method mainly consider the miRNA-disease association prediction as semi-supervised learning problem. SVAEMDA integrates disease semantic similarity, miRNA functional similarity and respective Gaussian interaction profile (GIP) similarities. The integrated similarities are used to learn the representations of diseases and miRNAs. SVAEMDA trains a variational autoencoder based predictor by using known miRNA-disease associations, with the form of concatenated dense vectors. Reconstruction probability of the predictor is used to measure the correlation of the miRNA-disease pairs. Experimental results show that SVAEMDA outperforms other stat-of-the-art methods. AUC values of SVAEMDA of global leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV) are 0.9464 and 0.9428 respectively. In addition, case studies of three common human diseases indicate that SVAEMDA obtains 100 percent of the top 50 predicted candidates in the benchmark databases. Therefore, SVAEMDA can efficiently and accurately predict the potential associations between diseases and miRNAs.

中文翻译：

---

基于变分自编码器的miRNA-疾病关联预测半监督学习方法

微小RNA（miRNA）是一类非编码RNA，在细胞生长、发育、分化和衰老等许多生物过程中发挥着关键作用。越来越多的研究表明，miRNA 与许多人类疾病密切相关。因此，miRNA与疾病关联的预测对于人类疾病的发病机制、诊断和干预的研究具有重要意义。然而，生物学实验方法通常在时间和金钱上都很昂贵，而计算方法可以提供一种有效的方法来推断潜在的疾病相关 miRNA。在这项研究中，我们提出了一种新的方法来预测潜在的 miRNA 与疾病的关联，称为 SVAEMDA。我们的方法主要将 miRNA-疾病关联预测视为半监督学习问题。SVAEMDA 整合了疾病语义相似性、miRNA 功能相似性和各自的高斯交互谱 (GIP) 相似性。综合相似性用于学习疾病和 miRNA 的表示。SVAEMDA 通过使用已知的 miRNA 疾病关联以串联密集向量的形式训练基于变分自动编码器的预测器。预测因子的重建概率用于测量 miRNA-疾病对的相关性。实验结果表明，SVAEMDA 优于其他最先进的方法。全局留一法交叉验证（LOOCV）和5倍交叉验证（5-fold CV）的SVAEMDA的AUC值分别为0.9464和0.9428。此外，三种常见人类疾病的案例研究表明，SVAEMDA 获得了基准数据库中前 50 名预测候选者的 100%。因此，SVAEMDA 可以高效准确地预测疾病与 miRNA 之间的潜在关联。