The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Inferring novel lncRNA–disease associations based on a random walk model of a lncRNA functional similarity network
Abstract
Accumulating evidence demonstrates that long non-coding RNAs (lncRNAs) play important roles in the development and progression of complex human diseases, and predicting novel human lncRNA–disease associations is a challenging and urgently needed task, especially at a time when increasing amounts of lncRNA-related biological data are available. In this study, we proposed a global network-based computational framework, RWRlncD, to infer potential human lncRNA–disease associations by implementing the random walk with restart method on a lncRNA functional similarity network. The performance of RWRlncD was evaluated by experimentally verified lncRNA–disease associations, based on leave-one-out cross-validation. We achieved an area under the ROC curve of 0.822, demonstrating the excellent performance of RWRlncD. Significantly, the performance of RWRlncD is robust to different parameter selections. Predictively highly-ranked lncRNA–disease associations in case studies of prostate cancer and Alzheimer's disease were manually confirmed by literature mining, providing evidence of the good performance and potential value of the RWRlncD method in predicting lncRNA–disease associations.
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Derrien, T., Johnson, R., Bussotti, G., Tanzer, A., Djebali, S., Tilgner, H., Guernec, G., Martin, D., Merkel, A., Knowles, D. G., et al. (2012) · Genome Research · doi:10.1101/gr.132159.111 · Cited by 5,061
Li, J.-H., Liu, S., Zhou, H., Qu, L.-H., & Yang, J.-H. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1248 · Cited by 4,686
Self-citationEvolution and Functions of Long Noncoding RNAs
Ponting, C. P., Oliver, P. L., & Reik, W. (2009) · Cell · doi:10.1016/j.cell.2009.02.006 · Cited by 4,208
The functional role of long non-coding RNA in human carcinomas
Gibb, E. A., Brown, C. J., & Lam, W. L. (2011) · Molecular Cancer · doi:10.1186/1476-4598-10-38 · Cited by 1,619
A new method to measure the semantic similarity of GO terms
Wang, J. Z., Du, Z., Payattakool, R., Yu, P. S., & Chen, C.-F. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm087 · Cited by 1,377
LncRNADisease: a database for long-non-coding RNA-associated diseases
Chen, G., Wang, Z., Wang, D., Qiu, C., Liu, M., Chen, X., Zhang, Q., Yan, G., & Cui, Q. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1099 · Cited by 982
Self-citationWang, D., Wang, J., Lu, M., Song, F., & Cui, Q. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq241 · Cited by 913
An Analysis of Human MicroRNA and Disease Associations
Lu, M., Zhang, Q., Deng, M., Miao, J., Guo, Y., Gao, W., & Cui, Q. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003420 · Cited by 902
Ideker, T., & Sharan, R. (2008) · Genome Research · doi:10.1101/gr.071852.107 · Cited by 817
Long noncoding RNAs and the genetics of cancer
Cheetham, S. W., Gruhl, F., Mattick, J. S., & Dinger, M. E. (2013) · British Journal of Cancer · doi:10.1038/bjc.2013.233 · Cited by 724
Cited by
Long non-coding RNAs and complex diseases: from experimental results to computational models
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LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases
Bao, Z., Yang, Z., Huang, Z., Zhou, Y., Cui, Q., & Dong, D. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky905 · Cited by 560
Prediction of lncRNA–disease associations based on inductive matrix completion
Lu, C., Yang, M., Luo, F., Wu, F.-X., Li, M., Pan, Y., Li, Y., & Wang, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty327 · Cited by 292
Xiao, X., Zhu, W., Liao, B., Xu, J., Gu, C., Ji, B., Yao, Y., Peng, L., & Yang, J. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00411 · Cited by 47
Metadata
- Authors
- Sun, J., Shi, H., Wang, Z., Zhang, C., Liu, L., Wang, L., He, W., Hao, D., Liu, S., & Zhou, M.
- Venue
- Molecular BioSystems
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-04-07
- Volume
- 10
- Issue
- 8
- Pages
- 2074-2081
- ISSN
- 1742-2051 (ISSN-L)
- 1742-206X
- OpenAlex
- W2002185065
- Cited by
- 289
- References
- 40
- FWCI
- 4.86
- Type
- journal article
- Language
- en
- Open access
- closed
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- Sun, J., Shi, H., Wang, Z., Zhang, C., Liu, L., Wang, L., He, W., Hao, D., Liu, S., & Zhou, M. (2014). Inferring novel lncRNA–disease associations based on a random walk model of a lncRNA functional similarity network. Molecular BioSystems. https://doi.org/10.1039/c3mb70608g
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