Long non-coding RNA (lncRNA) plays important roles in many biological and pathological processes, including transcriptional regulation and gene regulation. As lncRNA interacts with multiple proteins, predicting lncRNA-protein interactions (lncRPIs) is an important way to study the functions of lncRNA. Up to now, there have been a few works that exploit protein-protein interactions (PPIs) to help the prediction of new lncRPIs.
Here, researchers from Fudan University propose to boost the prediction of lncRPIs by fusing multiple protein-protein similarity networks (PPSNs). They first construct four PPSNs based on protein sequences, protein domains, protein GO terms and the STRING database respectively, then build a more informative PPSN by fusing these four constructed PPSNs. Finally, they predict new lncRPIs by a random walk method with the fused PPSN and known lncRPIs. The experimental results show that the new approach outperforms the existing methods.