Li Jianwei, Zhao Yingshu, Zhou Siyuan, Zhou Yuan, Lang Liying
Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China.
MOE Key Lab of Cardiovascular Sciences, Department of Biomedical Informatics, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University, Beijing, China.
Front Bioeng Biotechnol. 2020 Feb 6;8:27. doi: 10.3389/fbioe.2020.00027. eCollection 2020.
Although lncRNAs lack the potential to be translated into proteins directly, their complicated and diversiform functions make them as a window into decoding the mechanisms of human physiological activities. Accumulating experiment studies have identified associations between lncRNA dysfunction and many important complex diseases. However, known experimentally confirmed lncRNA functions are still very limited. It is urgent to build effective computational models for rapid predicting of unknown lncRNA functions on a large scale. To this end, valid similarity measure between known and unknown lncRNAs plays a vital role. In this paper, an original model was developed to calculate functional similarities between lncRNAs by integrating heterogeneous network data. In this model, a novel integrated network was constructed based on the data of four single lncRNA functional similarity networks (miRNA-based similarity network, disease-based similarity network, GTEx expression-based network and NONCODE expression-based network). Using the lncRNA pairs that share the target mRNAs as the benchmark, the results show that this integrated network is more effective than any single networks with an AUC of 0.736 in the cross validation, while the AUC of four single networks were 0.703, 0.733, 0.611, and 0.602. To implement our model, a web server named IHNLncSim was constructed for inferring lncRNA functional similarity based on integrating heterogeneous network data. Moreover, the modules of network visualization and disease-based lncRNA function enrichment analysis were added into IHNLncSim. It is anticipated that IHNLncSim could be an effective bioinformatics tool for the researches of lncRNA regulation function studies. IHNLncSim is freely available at http://www.lirmed.com/ihnlncsim.
虽然长链非编码RNA（lncRNAs）缺乏直接翻译成蛋白质的潜力，但其复杂多样的功能使其成为解码人类生理活动机制的一个窗口。越来越多的实验研究已经确定lncRNA功能障碍与许多重要的复杂疾病之间存在关联。然而，已知的经实验证实的lncRNA功能仍然非常有限。迫切需要建立有效的计算模型来大规模快速预测未知lncRNA的功能。为此，已知lncRNA与未知lncRNA之间有效的相似性度量起着至关重要的作用。本文开发了一种原始模型，通过整合异质网络数据来计算lncRNA之间的功能相似性。在该模型中，基于四个单一lncRNA功能相似性网络（基于miRNA的相似性网络、基于疾病的相似性网络、基于GTEx表达的网络和基于NONCODE表达的网络）的数据构建了一个新颖的整合网络。以共享靶mRNA的lncRNA对作为基准，结果表明，该整合网络比任何单一网络都更有效，在交叉验证中的AUC为0.736，而四个单一网络的AUC分别为0.703、0.733、0.611和0.602。为了实现我们的模型，构建了一个名为IHNLncSim的网络服务器，用于基于整合异质网络数据推断lncRNA功能相似性。此外，还在IHNLncSim中添加了网络可视化模块和基于疾病的lncRNA功能富集分析模块。预计IHNLncSim可能成为lncRNA调控功能研究的一种有效的生物信息学工具。可通过http://www.lirmed.com/ihnlncsim免费获取IHNLncSim。
