School of Information Science and Engineering at Shandong Normal University, Jinan, China.
School of Information Science and Engineering at Shandong Normal University, Jinan, China; Key Lab of Intelligent Computing & Information Security in Universities of Shandong, Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Institute of Biomedical Sciences, Shandong Normal University, Jinan, China.
Comput Biol Med. 2019 Jul;110:156-163. doi: 10.1016/j.compbiomed.2019.05.014. Epub 2019 May 25.
Uncovering disease-related microRNAs (miRNAs) by inferring miRNA-disease associations is of critical importance for understanding the pathogenesis of disease and carrying out treatment and prevention. Recently developed computational models for inferring miRNA-disease associations assume that functionally related miRNAs are associated with phenotypically similar diseases and hence infer miRNA-disease associations by using miRNA-miRNA and disease-disease similarities, which are concretely determined by mining existing biological resources. From the perspective of manifold learning, miRNA-miRNA similarities and disease-disease similarities determine a low-dimensional manifold for miRNAs and diseases, respectively, and the basic assumption of current computational models is equivalent to consistency between the manifold structures of miRNA and disease. In this paper, we propose a novel microRNA-disease inference framework (MAMDA) that explicitly takes advantage of this consistency property and infers miRNA-disease associations by aligning the manifold structure of miRNA with that of disease together with supervision of experimentally verified miRNA-disease associations. Based on three aspects, experimental results show that the proposed framework outperforms several representative state-of-the-art techniques. First, AUC values using k-fold cross-validation indicate that our method acquires more reliable predictions than four classical techniques (HGIMDA, HDMP, RLSMDA, and NCPMDA). Second, 48/48 predicted associations between miRNAs and breast cancer are validated with the HMDD and dbDEMC to show the effectiveness of predicting isolated diseases with unknown miRNAs. Third, two case studies of colon neoplasms and lung neoplasms validate the superior accuracy of MAMDA, with 48/50 and 48/50 predicted associations in the HMDD and dbDEMC, respectively.
通过推断 miRNA-疾病关联来揭示与疾病相关的 microRNAs（miRNAs）对于理解疾病的发病机制以及进行治疗和预防至关重要。最近开发的用于推断 miRNA-疾病关联的计算模型假设功能相关的 miRNAs 与表型相似的疾病有关联，因此通过使用 miRNA-miRNA 和疾病-疾病相似性来推断 miRNA-疾病关联，而 miRNA-miRNA 和疾病-疾病相似性则通过挖掘现有的生物资源来具体确定。从流形学习的角度来看，miRNA-miRNA 相似性和疾病-疾病相似性分别确定了 miRNAs 和疾病的低维流形，而当前计算模型的基本假设相当于 miRNA 和疾病的流形结构之间的一致性。在本文中，我们提出了一种新颖的 microRNA-疾病推断框架（MAMDA），该框架明确利用了这种一致性特性，并通过对齐 miRNA 的流形结构和疾病的流形结构，同时利用实验验证的 miRNA-疾病关联进行监督，来推断 miRNA-疾病关联。基于三个方面，实验结果表明，所提出的框架优于几种代表性的最新技术。首先，使用 k 折交叉验证的 AUC 值表明，与四种经典技术（HGIMDA、HDMP、RLSMDA 和 NCPMDA）相比，我们的方法获得了更可靠的预测。其次，与 HMDD 和 dbDEMC 验证了 48/48 个 miRNA 与乳腺癌之间的预测关联，以表明预测未知 miRNA 的孤立疾病的有效性。第三，结肠肿瘤和肺肿瘤的两个案例研究验证了 MAMDA 的卓越准确性，在 HMDD 和 dbDEMC 中分别预测了 48/50 和 48/50 个关联。
