Inferring lncRNA Functional Similarity Based on Integrating Heterogeneous Network Data

被引:7
作者
Li, Jianwei [1 ]
Zhao, Yingshu [1 ]
Zhou, Siyuan [1 ]
Zhou, Yuan [2 ]
Lang, Liying [1 ]
机构
[1] Hebei Univ Technol, Inst Computat Med, Sch Artificial Intelligence, Tianjin, Peoples R China
[2] Peking Univ, MOE Key Lab Cardiovasc Sci, Dept Biomed Informat, Ctr Noncoding RNA Med,Sch Basic Med Sci, Beijing, Peoples R China
来源
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY | 2020年 / 8卷 / 08期
基金
中国国家自然科学基金;
关键词
lncRNAs; miRNAs; expression profiles; mRNAs; lncRNA functional similarity; integrated heterogeneous network data; web server; LONG NONCODING RNAS; EXPRESSION; ANNOTATION; DISEASE; CERNA;
D O I
10.3389/fbioe.2020.00027
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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.
引用
收藏
页数:12
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