Abstract
Long non-coding RNA (lncRNA)–microRNA (miRNA) interactions are quickly emerging as important mechanisms underlying the functions of non-coding RNAs. Accordingly, predicting lncRNA–miRNA interactions provides an important basis for understanding the mechanisms of action of ncRNAs. However, the accuracy of the established prediction methods is still limited. In this study, we used structural consistency to measure the predictability of interactive links based on a bilayer network by integrating information for known lncRNA–miRNA interactions, an lncRNA similarity network, and an miRNA similarity network. In particular, by using the structural perturbation method, we proposed a framework called SPMLMI to predict potential lncRNA–miRNA interactions based on the bilayer network. We found that the structural consistency of the bilayer network was higher than that of any single network, supporting the utility of bilayer network construction for the prediction of lncRNA–miRNA interactions. Applying SPMLMI to three real datasets, we obtained areas under the curves of 0.9512 ± 0.0034, 0.8767 ± 0.0033, and 0.8653 ± 0.0021 based on 5-fold cross-validation, suggesting good model performance. In addition, the generalizability of SPMLMI was better than that of the previously established methods. Case studies of two lncRNAs (i.e., SNHG14 and MALAT1) further demonstrated the feasibility and effectiveness of the method. Therefore, SPMLMI is a feasible approach to identify novel lncRNA–miRNA interactions underlying complex biological processes.
Highlights
For a long time, our framework for understanding the nature of genetic programming in complex organisms was biased by the assumption that protein-coding genes are the majority of the bearers of genetic information
We calculated the structural consistency of three datasets (i.e., the EPLMI dataset provided by Huang, Chan & You (2018), SNFHGILMI dataset provided by Fan, Cui & Zhu (2020), and Structural Perturbation Method for predicting LncRNA-MiRNA Interactions (SPMLMI) dataset obtained in this study)
Each group of datasets was composed of an miRNA similarity network (MSnet ), an Long non-coding RNA (lncRNA) similarity network (LSnet ), and a bilayer network (Bilayer − net )
Summary
Our framework for understanding the nature of genetic programming in complex organisms was biased by the assumption that protein-coding genes are the majority of the bearers of genetic information. The key regulatory factors are non-coding RNAs (ncRNAs) (Huntzinger & Izaurralde, 2011; Rinn & Chang, 2012; Sabin, Delas & Hannon, 2013), which account for the vast majority of mammalian transcripts and range in length from 22 nucleotides to hundreds of kilobases. There are established principles that define classes of ncRNAs (e.g., tRNAs and miRNAs), it could be suggested that each ncRNA has a unique function (Cech & Steitz, 2014)
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