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The interaction of miRNA and lncRNA is known to be important for gene regulations. However, not many computational approaches have been developed to analyse known interactions and predict the unknown ones. Given that there are now more evidences that suggest that lncRNA-miRNA interactions are closely related to their relative expression levels in the form of a titration mechanism, researchers from analyzed the patterns in large-scale expression profiles of known lncRNA-miRNA interactions. From these uncovered patterns, they noticed that lncRNAs tend to interact collaboratively with miRNAs of similar expression profiles, and vice versa.
By representing known interaction between lncRNA and miRNA as a bipartite graph, the researchers propose here a technique, called EPLMI, to construct a prediction model from such a graph. EPLMI performs its tasks based on the assumption that lncRNAs that are highly similar to each other tend to have similar interaction or non-interaction patterns with miRNAs and vice versa. The effectiveness of the prediction model so constructed has been evaluated using the latest dataset of lncRNA-miRNA interactions. The results show that the prediction model can achieve AUCs of 0.8522 and 0.8447±0.0017 based on LOOCV and 5-fold cross validation. Using this model, the researchers show that lncRNA-miRNA interactions can be reliably predicted. They also show that they can use it to select the most likely lncRNA targets that specific miRNAs would interact with. They believe that the prediction models discovered by EPLMI can yield great insights for further research on ceRNA regulation network.
The flowchart of prediction process of EPLMI
Availability – Matlab codes and dataset are available at https://github.com/yahuang1991polyu/EPLMI/.
