Abstract
Accumulated studies have shown that environmental factors (EFs) can regulate the expression of microRNA (miRNA) which is closely associated with several diseases. Therefore, identifying miRNA-EF associations can facilitate the study of diseases. Recently, several computational methods have been proposed to explore miRNA-EF interactions. In this paper, a novel computational method, MEI-BRWMLL, is proposed to uncover the relationship between miRNA and EF. The similarities of miRNA-miRNA are calculated by using miRNA sequence, miRNA-EF interaction, and the similarities of EF-EF are calculated based on the anatomical therapeutic chemical information, chemical structure and miRNA-EF interaction. The similarity network fusion is used to fuse the similarity between miRNA and the similarity between EF, respectively. Further, the multiple-label learning and bi-random walk are employed to identify the association between miRNA and EF. The experimental results show that our method outperforms the state-of-the-art algorithms.
Highlights
There is increasing evidence demonstrating that phenotypes are associated with genetic factors (GFs) and environmental factors (EFs) [1,2]
One subset is used as test set and the remaining nine subsets are treated as training on the value of true positive rates (TPR) and false positive rates (FPR) and the area under the receiver operating characteristics (ROC) curve (AUC) is calculated to measure the set
Increasing studies have demonstrated that diseases have close relationship with GFs and EFs [48,49]. miRNAs are a group of important GFs which have been proved to play critical roles in many diseases [50,51]
Summary
There is increasing evidence demonstrating that phenotypes are associated with genetic factors (GFs) and environmental factors (EFs) [1,2]. Identifying the potential associations between GFs and EFs is useful for biologists to understand the molecular bases of diseases. Presented a computational approach (miREFRWR) to infer miRNA-EF interactions based on a random walk method. Jiang et al [23] constructed a small molecule-miRNA interaction network in 23 cancers and identified the miRNA-EF associations based on hypergeometric tests. Presented a computational framework based on an EF structure and disease similarity method to predict the interaction. Some approaches measure miRNA similarity and EF similarity by using network-based data only, which may result in a bias for ignoring the biological characteristics of miRNA and EF. Based on this assumption, a computational framework is developed to predict the interactions between miRNAs and EFs. Unlike traditional methods, we use different data sources to measure miRNA-miRNA similarity and EF-EF similarity. The experimental results show that our method is effective in inferring miRNA-environmental factor interactions
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