Zheng Xiaoxiong, Wang Yang, Tian Kai, Zhou Jiaogen, Guan Jihong, Luo Libo, Zhou Shuigeng
Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, 220 Handan Road, Shanghai, 200433, China.
School of Software, Jiangxi Normal University, 99 Ziyang Avenue, Nanchang, 330022, China.
BMC Bioinformatics. 2017 Oct 16;18(Suppl 12):420. doi: 10.1186/s12859-017-1819-1.
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.
长链非编码RNA（lncRNA）在许多生物学和病理学过程中发挥着重要作用，包括转录调控和基因调控。由于lncRNA与多种蛋白质相互作用，预测lncRNA-蛋白质相互作用（lncRPI）是研究lncRNA功能的重要途径。到目前为止，已有一些利用蛋白质-蛋白质相互作用（PPI）来辅助预测新的lncRPI的研究。
在本文中，我们提出通过融合多个蛋白质-蛋白质相似性网络（PPSN）来提高lncRPI的预测能力。具体而言，我们首先分别基于蛋白质序列、蛋白质结构域、蛋白质GO术语和STRING数据库构建四个PPSN，然后通过融合这四个构建好的PPSN构建一个信息量更大的PPSN。最后，我们使用融合后的PPSN和已知的lncRPI，通过随机游走方法预测新的lncRPI。我们的实验结果表明，新方法优于现有方法。
融合多个蛋白质-蛋白质相似性网络可以有效地提高lncRPI的预测性能。
