Prediction of Disease-Related miRNAs via Functional Network Information Propagation

被引：0
|
作者：
Li J.-H. [1 ]
Luo S.-Y. [1 ]
Zhang J.-Y. [1 ]
Kang Y. [1 ]
机构：
[1] School of Sino-Dutch Biomedical & Information Engineering, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2018年 / 39卷 / 03期
关键词：
Disease network; Functional network; MiRNA prediction; Network propagation; Random walk;
D O I：
10.12068/j.issn.1005-3026.2018.03.005
中图分类号：
学科分类号：
摘要：
In order to quickly find out disease-related miRNAs, PMBP algorithm was proposed for improving random walk based on functional network information propagation. Leave-one-out cross validation was utilized to evaluate the performance of the algorithm and finally a case was analyzed. The results showed that random walk is ineffective for diseases that have not yet been associated with miRNAs, but the miRNA can be effectively predicted by using disease similarities as prior information. For the diseases known to be related with miRNAs, PMPB achieves a better performance and the corresponding AUC value is 0.866. In the case study of breast cancer, the predicted top 50 miRNAs are confirmed to be associated with breast cancer, which indicates the validity of PMBP. © 2018, Editorial Department of Journal of Northeastern University. All right reserved.
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页码：325 / 328and344
相关论文
共 18 条
  • [1] Minju H., Narry K., Regulation of microRNA biogenesis, Nature Reviews Molecular Cell Biology, 15, 8, pp. 509-524, (2014)
  • [2] Zeng X.X., Zhang X., Zou Q., Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks, Briefings in Bioinformatics, 17, 2, pp. 193-203, (2016)
  • [3] Zhang F., Cui Q.-H., Bioinformatics methods and resources for the research on the relationship between MicroRNAs and human diseases, Progress in Physiological Sciences, 47, 3, pp. 203-209, (2016)
  • [4] Jiang Q., Wang G., Jin S., Et al., Predicting human microRNA-disease associations based on support vector machine, International Journal of Data Mining and Bioinformatics, 8, 3, pp. 282-293, (2013)
  • [5] Zou Q., Li J.J., Hong Q.Q., Et al., Prediction of MicroRNA-disease associations based on social network analysis methods, BioMed Research International, 2015, (2015)
  • [6] Chen X., Yan G.Y., Semi-supervised learning for potential human microRNA-disease associations inference, Scientific Report, 4, (2014)
  • [7] Chen X., Yan C.G., Zhang X.T., Et al., RBMMMDA: predicting multiple types of disease-microRNA associations, Scientific Report, 5, (2015)
  • [8] Chen H.L., Zhang Z.P., Similarity-based methods for potential human microRNA-disease association prediction, BMC Medical Genomics, 6, 12, pp. 1-9, (2013)
  • [9] Sun D.D., Li A., Feng H.Q., Et al., NTSMDA: prediction of miRNA-disease associations by integrating network topological similarity, Molecular BioSystems, 12, 7, pp. 2224-2232, (2016)
  • [10] Navlakha S., Kingsford C., The power of protein interaction networks for associating genes with diseases, Bioinformatics, 26, 8, pp. 1057-1063, (2010)
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