LPIHN – Predicting Long Noncoding RNA and Protein Interactions Using Heterogeneous Network Model

Recent study shows that long noncoding RNAs (lncRNAs) are participating in diverse biological processes and complex diseases. However, at present the functions of lncRNAs are still rarely known. In this study, researchers from the University of Science and Technology of China propose a network-based computational method, which is called lncRNA-protein interaction prediction based on Heterogeneous Network Model (LPIHN), to predict the potential lncRNA-protein interactions.

First, they construct a heterogeneous network with the use of protein-protein interaction (PPI), lncRNAs expression similarity, and known lncRNA-protein interactions. Then, a random walk with restart is implemented on the heterogeneous network to infer novel lncRNA-protein interactions. They compare the performance with two network-based methods including PRIoritizatioN and Complex Elucidation (PRINCE) and the random walk based method (RWR). In the leave-one-out cross validation (LOOCV) test we implement, LPIHN outperforms PRINCE and RWR by a significant margin. Moreover, we identify several lncRNA-protein interactions that are supported by evidence in recent literature or database, which shows the practical value of our method.

Case study results on lncRNA-protein interaction predictions

Purple and red nodes indicate lncRNAs and proteins, respectively, blue edges known interactions, and red dotted edges newly predicted interactions with 5 highest scores.