School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.
Genes (Basel). 2019 Jan 24;10(2):80. doi: 10.3390/genes10020080.
In discovering disease etiology and pathogenesis, the associations between MicroRNAs (miRNAs) and diseases play a critical role. Given known miRNA-disease associations (MDAs), how to uncover potential MDAs is an important problem. To solve this problem, most of the existing methods regard known MDAs as positive samples and unknown ones as negative samples, and then predict possible MDAs by iteratively revising the negative samples. However, simply viewing unknown MDAs as negative samples introduces erroneous information, which may result in poor predication performance. To avoid such defects, we present a novel method using only positive samples to predict MDAs by latent features extraction (LFEMDA). We design a new approach to construct the miRNAs similarity matrix. LFEMDA integrates the disease similarity matrix, the known MDAs and the miRNAs similarity matrix to identify potential MDAs. By introducing miRNAs and diseases knowledge as the auxiliary variables, the method can converge to give the optimal solution in each iteration. We conduct experiments on high-association diseases and new diseases datasets, in which our method shows better performance than that of other methods. We also carry out a case study on breast neoplasms to further demonstrate the capacity of our method in uncovering potential MDAs.
在发现疾病病因和发病机制方面，MicroRNAs（miRNAs）与疾病之间的关联起着关键作用。鉴于已知的 miRNA-疾病关联（MDAs），如何发现潜在的 MDAs 是一个重要问题。为了解决这个问题，大多数现有的方法将已知的 MDAs 视为正样本，将未知的 MDAs 视为负样本，然后通过反复修改负样本来预测可能的 MDAs。然而，简单地将未知的 MDAs 视为负样本会引入错误信息，可能导致预测性能不佳。为了避免这种缺陷，我们提出了一种新颖的方法，仅使用正样本通过潜在特征提取（LFEMDA）来预测 MDAs。我们设计了一种新方法来构建 miRNAs 相似性矩阵。LFEMDA 整合了疾病相似性矩阵、已知的 MDAs 和 miRNAs 相似性矩阵，以识别潜在的 MDAs。通过引入 miRNAs 和疾病知识作为辅助变量，该方法可以在每次迭代中收敛到最优解。我们在高关联疾病和新疾病数据集上进行了实验，结果表明，我们的方法优于其他方法。我们还对乳腺肿瘤进行了案例研究，进一步证明了我们的方法在发现潜在 MDAs 方面的能力。
