3 years ago

Fusing multiple protein-protein similarity networks to effectively predict lncRNA-protein interactions

Fusing multiple protein-protein similarity networks to effectively predict lncRNA-protein interactions
Libo Luo, Xiaoxiong Zheng, Shuigeng Zhou, Yang Wang, Jiaogen Zhou, Kai Tian, Jihong Guan
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. In this paper, we propose to boost the prediction of lncRPIs by fusing multiple protein-protein similarity networks (PPSNs). Concretely, we 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, we predict new lncRPIs by a random walk method with the fused PPSN and known lncRPIs. Our experimental results show that the new approach outperforms the existing methods. Fusing multiple protein-protein similarity networks can effectively boost the performance of predicting lncRPIs.
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