Prediction Algorithm of Association Between miRNAs and Diseases Based on Deep Learning
WANG Lei1, XU Tao1, SONG Chuan-dong1, WANG Hai-feng1, YOU Zhu-hong2, SONG Ke-jian3, YAN Xin4
1. College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong 277100, China;
2. Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China;
3. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China;
4. School of Foreign Languages, Zaozhuang University, Zaozhuang, Shandong 277100, China
Abstract:Numerous studies have shown that microRNA (miRNA) plays important role in the study of human complex diseases.Identifying the association between miRNAs and diseases is important for improving the therapeutic level of complex diseases.However,traditional experimental is often limited to small-scale and high-cost,so computational simulation is urgently needed to quickly and effectively predict the potential miRNAs-disease associations.In this study,a new method is proposed to predict the miRNA-disease association by combining deep learning stacked automatic encoder algorithm with rotation forest classifier.This method can effectively extract high-level features that combine disease semantic similarity,miNRA functional similarity and miRNA sequence information,and accurately classify them.In the cross-validation experiment,this method achieved 90.30% prediction accuracy on the HMDD v3.0 dataset.Furthermore,we have also done case studies on Breast Neoplasms.As a result,28 of the top 30 miRNA-disease associations were confirmed.These excellent results indicate that this method is an effective tool for predicting miRNA-disease associations,and can provide highly reliable candidate miRNAs for biological experiments.
王磊, 徐涛, 宋传东, 王海峰, 尤著宏, 宋克俭, 闫欣. 基于深度学习的miRNA与疾病相关性预测算法[J]. 电子学报, 2020, 48(5): 870-877.
WANG Lei, XU Tao, SONG Chuan-dong, WANG Hai-feng, YOU Zhu-hong, SONG Ke-jian, YAN Xin. Prediction Algorithm of Association Between miRNAs and Diseases Based on Deep Learning. Acta Electronica Sinica, 2020, 48(5): 870-877.
[1] Bartel D P.MicroRNAs:genomics,biogenesis,mechanism,and function[J].Cell,2004,116(2):281-297.
[2] Gunter M,Thomas T.Mechanisms of gene silencing by double-stranded RNA[J].Nature,2004,431(7006):343-349.
[3] Bang C,Fiedler J,Thum T.Cardiovascular importance of the microRNA-23/27/24 family[J].Microcirculation,2012,19(3):208-214.
[4] Li Y,Qiu C,Tu J,Geng B,Yang J,Jiang T,Cui Q.HMDD v2.0:a database for experimentally supported human microRNA and disease associations[J].Nucleic Acids Research,2013,42(D1):D1070-D1074.
[5] Wang L,You Z-H,Chen X,Li Y-M,Dong Y-N,Li L-P,Zheng K.LMTRDA:Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities[J].PLoS Computational Biology,2019,15(3):e1006865.
[6] Wang D,Wang J,Lu M,Song F,Cui Q.Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases[J].Bioinformatics,2010,26(13):1644-1650.
[7] van Laarhoven T,Nabuurs SB,Marchiori E.Gaussian interaction profile kernels for predicting drug-target interaction[J].Bioinformatics,2011,27(21):3036-3043.
[8] Wang L,You Z-H,Xia S-X,Liu F,Chen X,Yan X,Zhou Y.Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier[J].Journal of Theoretical Biology,2017,418:105-110.
[9] Wang L,Wang H-F,Liu S-R,Yan X,Song K-J.Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest[J].Scientific Reports,2019,9(1):9848.
[10] Wang L,Yan X,Liu M-L,Song K-J,Sun X-F,Pan W-W.Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method[J].Journal of Theoretical Biology,2019,461:230-238.
[11] Wang L,You Z-H,Xia S-X,Chen X,Yan X,Zhou Y,Liu F.An improved efficient rotation forest algorithm to predict the interactions among proteins[J].Soft Computing,2018,22(10):3373-3381.
[12] Wang L,You Z-H,Yan X,Xia S-X,Liu F,Li L-P,Zhang W,Zhou Y.Using two-dimensional principal component analysis and rotation forest for prediction of protein-protein interactions[J].Scientific Reports,2018,8(1):12874.
[13] Chen X,Xie D,Wang L,Zhao Q,Liu H.BNPMDA:Bipartite network projection for MiRNA-Disease association prediction[J].Bioinformatics,2018,34(18):3178-3186.
[14] Yu H,Chen X,Lu L.Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm[J].Scientific Reports,2017,7:43792.
[15] Chen X,Yin J,Qu J,Huang L.MDHGI:Matrix decomposition and heterogeneous graph inference for miRNA-disease association prediction[J].PLoS Computational Biology,2018,14(8):e1006418.
[16] Li J-Q,Rong Z-H,Chen X,Yan G-Y,You Z-H.MCMDA:Matrix completion for MiRNA-disease association prediction[J].Oncotarget,2017,8(13):21187.
[17] Yang Y,Fu X,Qu W,Xiao Y,Shen H-B.MiRGOFS:a GO-based functional similarity measurement for miRNAs,with applications to the prediction of miRNA subcellular localization and miRNA-disease association[J].Bioinformatics,2018,34(20):3547-3556.
[18] Jiang Q,Wang Y,Hao Y,Juan L,Teng M,Zhang X,Li M,Wang G,Liu Y.miR2Disease:a manually curated database for microRNA deregulation in human disease[J].Nucleic Acids Research,2008,37(suppl_1):D98-D104.