Mamda: Inferring Microrna-Disease Associations with Manifold Alignment
Mamda: Inferring Microrna-Disease Associations with Manifold Alignment
复制标题
MAMDA：通过流形比对推断 microRNA 疾病关联
DOI：
10.1016/j.compbiomed.2019.05.014
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发表时间：
2019
影响因子：
7.7
通讯作者：
Xiao Rui
中科院分区：
文献类型：
--
作者：
Yan Fang;Zheng Yuanjie;Jia Weikuan;Hou Sujuan;Xiao Rui
Uncovering disease-related microRNAs (miRNAs) by inferring miRNA-disease associations is of critical im-portance 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 re-presentative 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.
