Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Aug 19:12:712170.
doi: 10.3389/fgene.2021.712170. eCollection 2021.

Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks

Yuhua Yao  1   2   3 , Binbin Ji  4 , Yaping Lv  1 , Ling Li  5 , Ju Xiang  6   7   8 , Bo Liao  1 , Wei Gao  9
Affiliations

Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks

Yuhua Yao et al. Front Genet. .

Abstract

Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA-disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective computational models. We developed a random walk algorithm with restart on multiplex and heterogeneous networks of lncRNAs and diseases to predict lncRNA-disease associations (MHRWRLDA). First, multiple disease similarity networks are constructed by using different approaches to calculate similarity scores between diseases, and multiple lncRNA similarity networks are also constructed by using different approaches to calculate similarity scores between lncRNAs. Then, a multiplex and heterogeneous network was constructed by integrating multiple disease similarity networks and multiple lncRNA similarity networks with the lncRNA-disease associations, and a random walk with restart on the multiplex and heterogeneous network was performed to predict lncRNA-disease associations. The results of Leave-One-Out cross-validation (LOOCV) showed that the value of Area under the curve (AUC) was 0.68736, which was improved compared with the classical algorithm in recent years. Finally, we confirmed a few novel predicted lncRNAs associated with specific diseases like colon cancer by literature mining. In summary, MHRWRLDA contributes to predict lncRNA-disease associations.

Keywords: association; disease; lncRNA; networks; predict; random walk.

PubMed Disclaimer

Conflict of interest statement

BJ was employed by Geneis Beijing Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a past co-authorship with one of the authors JX.

Figures

FIGURE 1
FIGURE 1
The framework of MHRWRLDA.
FIGURE 2
FIGURE 2
The ROC curves of MHRWRLDA, KATZLDA, BPLLDA, and LRLSLDA based on global LOOCV.
FIGURE 3
FIGURE 3
The PR curves of MHRWRLDA, KATZLDA, BPLLDA, and LRLSLDA based on global LOOCV.
FIGURE 4
FIGURE 4
The network of diseases and lncRNAs were made by Cytoscape. (A) The network of novel lncRNAs related to colon cancer, hepatocellular carcinoma, and breast cancer. (B) The network of novel diseases related to lncRNA MALAT1, PVT1, and MEG3.
See this image and copyright information in PMC

References

    1. Akerman I., Tu Z., Beucher A., Rolando D. M. Y., Sauty-Colace C., Benazra M., et al. (2017). Human pancreatic β Cell lncRNAs control cell-specific regulatory networks. Cell Metab. 25 400–411. 10.1016/j.cmet.2016.11.016 - DOI - PMC - PubMed
    1. Barsytelovejoy D., Lau S. K., Boutros P. C., Khosravi F., Jurisica I., Andrulis I. L., et al. (2006). The c-Myc oncogene directly induces the H19 noncoding RNA by allele-specific binding to potentiate tumorigenesis. Cancer Res. 66 5330–5337. 10.1158/0008-5472.can-06-0037 - DOI - PubMed
    1. Bellucci M., Agostini F., Masin M., Tartaglia G. G. (2011). Predicting protein associations with long noncoding RNAs. Nat. Methods 8 444–445. 10.1038/nmeth.1611 - DOI - PubMed
    1. Chen X. (2015a). KATZLDA: KATZ measure for the lncRNA-disease association prediction. Sci. Rep. 5:16840. - PMC - PubMed
    1. Chen X. (2015b). Predicting lncRNA–disease associations and constructing lncRNA functional similarity network based on the information of miRNA. Sci. Rep. 5:13186. - PMC - PubMed

LinkOut - more resources