Abstract
Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an important topic in the field of biology, medicine, and bioinformatics. In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease associations into a heterogeneous graph. HGIMDA obtained AUCs of 0.8781 and 0.8077 based on global and local leave-one-out cross validation, respectively. Furthermore, HGIMDA was applied to three important human cancers for performance evaluation. As a result, 90% (Colon Neoplasms), 88% (Esophageal Neoplasms) and 88% (Kidney Neoplasms) of top 50 predicted miRNAs are confirmed by recent experiment reports. Furthermore, HGIMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcome the important limitations of many previous computational models.
Highlights
MiRNAs are one category of short non-coding RNAs (~22nt) which could inhibit the protein production and gene expression through binding to the 3’-UTRs of the target mRNAs at the post-transcriptional and translational level [1,2,3,4]
In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease associations into a heterogeneous graph
In this paper, considering the hypothesis that functional similar miRNAs are likely to be involved in similar diseases and vice versa, we presented the computational model of HGIMDA to predict new human complex diseases related miRNAs by integrating Gaussian interaction profile kernel similarity, disease semantic similarity, miRNA functional similarity, and known miRNA-disease associations into a heterogeneous graph
Summary
MiRNAs are one category of short non-coding RNAs (~22nt) which could inhibit the protein production and gene expression through binding to the 3’-UTRs of the target mRNAs at the post-transcriptional and translational level [1,2,3,4]. Calin et al firstly clarified that miR-15 and miR-16 are deleted in more than half cases of B-cell chronic lymphocytic leukemia (B-CLL), and this discovery become the first evidence for the fact that miRNAs are involved in cancer formation [29]. He et al firstly reported that there are links between the enhanced expression of miR-17 cluster in B-cell lymphomas and the development of c-Mycinduced tumorigenesis [30]. Considering vast amount of miRNA-related biological datasets has been generated, it is urgent to develop powerful computational models to predict novel human disease-miRNA associations [34,35,36,37,38,39,40,41,42,43,44,45,46]
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