Fusing multiple protein-protein similarity networks can effectively boost the performance of predicting lncRNA-protein interactions

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.


Zheng X, Wang Y, Tian K, Zhou J, Guan J, Luo L, Zhou S. (2017) Fusing multiple protein-protein similarity networks to effectively predict lncRNA-protein interactions. BMC Bioinformatics 18(Suppl 12):420. [article]

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