Research Papers:

IntNetLncSim: an integrative network analysis method to infer human lncRNA functional similarity

Liang Cheng, Hongbo Shi, Zhenzhen Wang, Yang Hu, Haixiu Yang, Chen Zhou, Jie Sun and Meng Zhou _

PDF  |  HTML  |  How to cite

Oncotarget. 2016; 7:47864-47874. https://doi.org/10.18632/oncotarget.10012

Metrics: PDF 1571 views  |   HTML 2577 views  |   ?  


Liang Cheng1,*, Hongbo Shi1,*, Zhenzhen Wang1,*, Yang Hu2, Haixiu Yang1, Chen Zhou1, Jie Sun1, Meng Zhou1

1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China

2School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, PR China

*These authors have contributed equally to this work

Correspondence to:

Meng Zhou, email: [email protected]

Jie Sun, email: [email protected]

Keywords: long non-coding RNAs, lncRNA functional similarity, integrated network, lncRNA-disease associations

Received: March 03, 2016    Accepted: May 23, 2016    Published: June 14, 2016


Increasing evidence indicated that long non-coding RNAs (lncRNAs) were involved in various biological processes and complex diseases by communicating with mRNAs/miRNAs each other. Exploiting interactions between lncRNAs and mRNA/miRNAs to lncRNA functional similarity (LFS) is an effective method to explore function of lncRNAs and predict novel lncRNA-disease associations. In this article, we proposed an integrative framework, IntNetLncSim, to infer LFS by modeling the information flow in an integrated network that comprises both lncRNA-related transcriptional and post-transcriptional information. The performance of IntNetLncSim was evaluated by investigating the relationship of LFS with the similarity of lncRNA-related mRNA sets (LmRSets) and miRNA sets (LmiRSets). As a result, LFS by IntNetLncSim was significant positively correlated with the LmRSet (Pearson correlation γ2=0.8424) and LmiRSet (Pearson correlation γ2=0.2601). Particularly, the performance of IntNetLncSim is superior to several previous methods. In the case of applying the LFS to identify novel lncRNA-disease relationships, we achieved an area under the ROC curve (0.7300) in experimentally verified lncRNA-disease associations based on leave-one-out cross-validation. Furthermore, highly-ranked lncRNA-disease associations confirmed by literature mining demonstrated the excellent performance of IntNetLncSim. Finally, a web-accessible system was provided for querying LFS and potential lncRNA-disease relationships: http://www.bio-bigdata.com/IntNetLncSim.

Creative Commons License All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 License.
PII: 10012