A Method for Predicting Disease-Related MicroRNAs Based on Topological Information
GAO Peng1, CHEN Zhi-hua2
1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;
2. Institute of Computing Science and Technology, Guangzhou University, Guangzhou, Guangdong 510006, China
Abstract: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.
[1] A Ambros V.The functions of animal microRNAs[J].Nature,2004,431(7006):350-355.
[2] Lü L,Zhou T.Link prediction in complex networks:A Survey[J].Physica A,2011,390(6):1150-1170.
[3] Jiang Q,et al.Prioritization of disease microRNAs through a human phenome-microRNAome network[J].BMC Systems Biology,2010,4(Suppl 1):S2.
[4] Xuan P,et al.Correction:Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors[J].PloS One,2013,8(8):e70204.1971.
[5] Chen X,Liu M,Yan G.RWRMDA:predicting novel human mi-croRNA-disease associations[J].Molecular BioSystems,2012,8(10):2792-2798.
[6] Shi H,et al.Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes[J].BMC Systems Biology,2013,7(1):101-101.
[7] Xuan P,et al.Prediction of potential disease-associated microRNAs based on random walk[J].Bioinformatics,2015,31(11):1805-1815.
[8] Jiang Q,Wang G,Jin S,et al.Predicting human microRNA-disease associations based on support vector machine[J].International Journal of Data Mining and Bioinformatics,2013,8(3):282-293.
[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[J].Molecular Cancer Therapeutics,2011,10(10):1857-1866.
[10] Chen X,Yan G Y.Semi-supervised learning for potential human microRNA-disease associations inference[J].Scientific Reports,2014,4(1):5501.
[11] Chen B,et al.Assessing drug target association using semantic linked data[J].PLoS Comput Biol,2012a,8(7):e1002574.
[12] Perozzi B,et al.Deepwalk:Online learning of social representations[A].Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C].New York,USA:ACM,2014.701-710.
[13] ZHOU Z H,FENG J.Deep forest:towards an alternative to deep neural networks[A].Twenty-Sixth International Joint Conference on Artificial Intelligence[C].Melbourne,Australia:IJCAI,2017.3553-3559.
[14] Ding,Pingjian,et al.Human disease MiRNA inference by combining target information based on heterogeneous manifolds[J].Journal of Biomedical Informatics,2018,80:26-36.
[15] L Cheng,et al,SIDD:a semantically integrated database towards a global view of human disease[J].PloS One,2013,8(10):e75504.
[16] C H Chou,et al.miRTarBase 2016:updates to the experimentally validated miRNA-target interactions database[J].Nucl Acids Res,2015,44 (D1):D239-D247.
[17] Huang Z,et al.HMDD v3.0:a database for experimentally supported human microRNA-disease associations[J].Nucleic Acids Research,2019,47(D1):D1013-D1017.
[18] 李敏,王晓桐,罗慧敏,孟祥茂,王建新.随机游走技术在网络生物学中的研究进展[J].电子学报,2018,46(8):2035-2048. LI Min,WANG Xiao-tong,LUO Hui-min,MENG Xiang-mao,WANG Jian-xin.Progress on random walk and its application in network biology[J].Acta Electronica Sinica,2018,46(8):2035-2048.(in Chinese)
[19] Mnih A,Hinton.A scalable hierarchical distributed language model[A].Proceedings of the 21st International Conference on Neural Information Processing Systems[C].British Columbia,Canada:ACM,2008,1081-1088.
[20] X Chen,et al.WBSMDA:within and between score for MiRNA-disease association prediction[J].Sci Reports,2016,6(1):21106.
[21] J.Luo,et al.Collective prediction of disease-associated miRNAs based on transduction learning[J].IEEE/ACM Trans Comput Biol Bioinform,2016,14(6):1468-1475.
[22] Yang Z.dbDEMC:a database of differentially expressed miRNAs in human cancers[J].BMC Genomics,2010,11(Suppl 4):S5.
[23] Jiang Q,et al.miR2Disease:a manually curated database for microRNA deregulation in human disease[J].Nucleic Acids Research,2009,37(suppl_1):D98-D104.