A Method for Predicting Disease-Related MicroRNAs Based on Topological Information

被引：0
|
作者：
Gao P. [1 ]
Chen Z.-H. [2 ]
机构：
[1] School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, Hubei
[2] Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, Guangdong
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 02期
关键词：
Heterogeneous network; Link prediction; Machine learning; miRNA; Network emmbedding; Topology information;
D O I：
10.3969/j.issn.0372-2112.2020.02.016
中图分类号：
学科分类号：
摘要：
Studies show that mutations or abnormalities in micRNAs can lead to many diseases, and the identification of disease-associated microRNAs (miRNAs) can help diagnose and treat related diseases. However, it is costly and long-term to obtain accurate correlations through biological experiments. Therefore, this paper proposes a machine learning method (HNDLM) that uses network topology information to predict disease-miRNA associations. HNDLM avoids the construction of similarity networks, but applies the network embedding method proposed in recent years to biological networks. Experimental results show that HNDLM performs better than MIDPE, MIDP, WBSMDA, RLSMDA, CPTL, HDMP classical algorithms in accuracy and AUC value. In case study, the top 30 candidate miRNAs recommended by HNDLM can be confirmed by previous experiments. HNDLM can discover the potential disease-miRNA relationship and help to further study the pathogenesis of the disease, promote the development of bioinformatics. © 2020, Chinese Institute of Electronics. All right reserved.
引用
收藏
页码：333 / 340
页数：7
相关论文
共 23 条
  • [1] Ambros V., The functions of animal microRNAs, Nature, 431, 7006, pp. 350-355, (2004)
  • [2] Lu L., Zhou T., Link prediction in complex networks: A Survey, Physica A, 390, 6, pp. 1150-1170, (2011)
  • [3] Jiang Q., Et al., Prioritization of disease microRNAs through a human phenome-microRNAome network, BMC Systems Biology, 4, (2010)
  • [4] Xuan P., Et al., Correction: Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors, PloS One, 8, 8, (2013)
  • [5] Chen X., Liu M., Yan G., RWRMDA: predicting novel human mi-croRNA-disease associations, Molecular BioSystems, 8, 10, pp. 2792-2798, (2012)
  • [6] Shi H., Et al., Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes, BMC Systems Biology, 7, 1, (2013)
  • [7] Xuan P., Et al., Prediction of potential disease-associated microRNAs based on random walk, Bioinformatics, 31, 11, pp. 1805-1815, (2015)
  • [8] 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)
  • [9] Xu J., Li C.X., Lv J.Y., Et al., Prioritizing candidate disease mirnas by topological features in the mirna target-dysregulated network: Case study of prostate cancer, Molecular Cancer Therapeutics, 10, 10, pp. 1857-1866, (2011)
  • [10] Chen X., Yan G.Y., Semi-supervised learning for potential human microRNA-disease associations inference, Scientific Reports, 4, 1, (2014)
123