Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (2): 276-287.doi: 10.1007/s11390-021-0740-2

Special Issue: Emerging Areas

• Special Section on AI and Big Data Analytics in Biology and Medicine • Previous Articles     Next Articles

Logistic Weighted Profile-Based Bi-Random Walk for Exploring MiRNA-Disease Associations

Ling-Yun Dai, Member, CCF, Jin-Xing Liu*, Senior Member, CCF, Rong Zhu, Member, CCF, Juan Wang, Member, CCF, and Sha-Sha Yuan, Member, CCF        

  1. School of Computer Science, Qufu Normal University, Rizhao 276826, China
  • Received:2020-07-13 Revised:2021-03-09 Online:2021-03-05 Published:2021-04-01
  • Contact: Jin-Xing Liu
  • About author:Ling-Yun Dai received her B.S. degree in electronic information science and technology from the Physics Department, Shandong Normal University, Jinan, in 2001, and her M.S. degree in communication and information systems from School of Information Science and Engineering, Shandong University, Jinan, in 2004. Now she is an associate professor at School of Computer Science, Qufu Normal University, Rizhao. Her research interests include pattern recognition, machine learning, and bioinformatics.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61902215, 61872220 and 61701279.

MicroRNAs (miRNAs) exert an enormous influence on cell differentiation, biological development and the onset of diseases. Because predicting potential miRNA-disease associations (MDAs) by biological experiments usually requires considerable time and money, a growing number of researchers are working on developing computational methods to predict MDAs. High accuracy is critical for prediction. To date, many algorithms have been proposed to infer novel MDAs. However, they may still have some drawbacks. In this paper, a logistic weighted profile-based bi-random walk method (LWBRW) is designed to infer potential MDAs based on known MDAs. In this method, three networks (i.e., a miRNA functional similarity network, a disease semantic similarity network and a known MDA network) are constructed first. In the process of building the miRNA network and the disease network, Gaussian interaction profile (GIP) kernel is computed to increase the kernel similarities, and the logistic function is used to extract valuable information and protect known MDAs. Next, the known MDA matrix is preprocessed by the weighted K-nearest known neighbours (WKNKN) method to reduce the number of false negatives. Then, the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network. Finally, the predictive ability of the LWBRW method is confirmed by the average AUC of 0.939 3 (0.006 1) in 5-fold cross-validation (CV) and the AUC value of 0.976 3 in leave-one-out cross-validation (LOOCV). In addition, case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.

Key words: miRNA-disease association; logistic function; Gaussian interaction profile; weighted K-nearest known neighbour; bi-random walk;

[1] Akhtar M M, Micolucci L, Islam S M et al. Bioinformatic tools for microRNA dissection. Nucleic Acids Research, 2016, 44(1):24-44. DOI:10.1093/nar/gkv1221.
[2] Miska E A. How microRNAs control cell division, differentiation and death. Current Opinion in Genetics & Development, 2005, 15(5):563-568. DOI:10.1016/j.gde.2005.08.005.
[3] Cheng A M, Byrom M W, Shelton J et al. Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Research, 2005, 33(4):1290-1297. DOI:10.1093/nar/gki200.
[4] Xu P, Guo M, Hay B A. MicroRNAs and the regulation of cell death. TRENDS in Genetics, 2004, 20(12):617-624. DOI:10.1016/j.tig.2004.09.010.
[5] Alshalalfa M, Alhajj R. Using context-specific effect of miRNAs to identify functional associations between miRNAs and gene signatures. BMC Bioinformatics, 2013, 14(Suppl 12):Article No. S1. DOI:10.1186/1471-2105-14-S12-S1.
[6] Fesler A, Zhai H, Ju J. miR-129 as a novel therapeutic target and biomarker in gastrointestinal cancer. OncoTargets and Therapy, 2004, 7:1481-1485. DOI:10.2147/OTT.S65548.
[7] Wu C, Li M, Hu C et al. Clinical significance of serum miR-223, miR-25 and miR-375 in patients with esophageal squamous cell carcinoma. Molecular Biology Reports, 2014, 41(3):1257-1266. DOI:10.1007/s11033-013-2970-z.
[8] Xiong S W, Lin T X, Xu K W et al. MicroRNA-335 acts as a candidate tumor suppressor in prostate cancer. Pathology Oncol. Res., 2013, 19(3):529-537. DOI:10.1007/s12253-013-9613-5.
[9] Zhao H, Zhang J, Shao H et al. miRNA-340 inhibits osteoclast differentiation via repression of MITF. Bioscience Reports, 2017, 37(4):Article No. BSR20170302. DOI:10.1042/BSR20170302.
[10] He F, Lv P, Zhao X et al. Predictive value of circulating miR-328 and miR-134 for acute myocardial infarction. Molecular and Cellular Biochemistry, 2014, 394(1/2):137-144. DOI:10.1007/s11010-014-2089-0.
[11] Chen X, Xie D, Zhao Q et al. MicroRNAs and complex diseases:From experimental results to computational models. Briefings in Bioinformatics, 2019, 20(2):515-539. DOI:10.1093/bib/bbx130.
[12] Chen X, Sun L G, Zhao Y. NCMCMDA:MiRNA-disease association prediction through neighborhood constraint matrix completion. Briefings in Bioinformatics, 2021, 22(1):485-496. DOI:10.1093/bib/bbz159.
[13] Chen X, Zhu C C, Yin J. Ensemble of decision tree reveals potential miRNA-disease associations. PLoS Computational Biology, 2019, 15(7):Article No. e1007209. DOI:10.1371/journal.pcbi.1007209.
[14] Chen X, Huang L, Xie D, Zhao Q. EGBMMDA:Extreme gradient boosting machine for MiRNA-disease association prediction. Cell Death & Disease, 2018, 9(1):Article No. 3. DOI:10.1038/s41419-017-0003-x.
[15] Huang F, Yue X, Xiong Z K et al. Tensor decomposition with relational constraints for predicting multiple types of microRNA-disease associations. Briefings in Bioinformatics. DOI:10.1093/bib/bbaa140.
[16] Zhang W, Li Z S, Guo W Z et al. A fast linear neighborhood similarity-based network link inference method to predict microRNA-disease associations. IEEE/ACM Transactions on Computational Biology and Bioinformatics. DOI:10.1109/TCBB.2019.2931546.
[17] Chen M, Liao B, Li Z. Global similarity method based on a two-tier random walk for the prediction of microRNAdisease association. Scientific Reports, 2018, 8(1):Article No. 6481. DOI:10.1038/s41598-018-24532-7.
[18] Gao Y L, Cui Z, Liu J X et al. NPCMF:Nearest profilebased collaborative matrix factorization method for predicting miRNA-disease associations. BMC Bioinformatics, 2019, 20(1):Article No. 353. DOI:10.1186/s12859-019-2956-5.
[19] Yin M M, Cui Z, Gao Y L et al. LWPCMF:Logistic weighted profile-based collaborative matrix factorization for predicting MiRNA-disease associations. IEEE/ACM Transactions on Computational Biology and Bioinformatics. DOI:10.1109/TCBB.2019.2937774.
[20] Jiang Q, Hao Y, Wang G et al. Prioritization of disease microRNAs through a human phenome-microRNAome network. BMC Systems Biology, 2010, 4(Suppl 1):Article No. S2. DOI:10.1186/1752-0509-4-S1-S2.
[21] Xuan P, Han K, Guo M et al. Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PLoS ONE, 2013, 8(8):Article No. e70204. DOI:10.1371/journal.pone.0070204.
[22] Xuan P, Han K, Guo Y et al. Prediction of potential diseaseassociated microRNAs based on random walk. Bioinformatics, 2015, 31(11):1805-1815. DOI:10.1093/bioinformatics/btv039.
[23] Chen X, Liu M X, Yan G Y. RWRMDA:Predicting novel human microRNA-disease associations. Molecular BioSystems, 2012, 8(10):2792-2798. DOI:10.1039/c2mb25180a.
[24] Mørk S, Pletscher-Frankild S, Palleja A et al. Protein-driven inference of miRNA-disease associations. Bioinformatics, 2014, 30(3):392-397. DOI:10.1093/bioinformatics/btt677.
[25] Shi H B, Xu J, Zhang G D et al. Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes. BMC Systems Biology, 2013, 7(1):Article No. 101. DOI:10.1186/1752-0509-7-101.
[26] Chen X, Niu Y W, Wang G H, Yan G Y. MKR-MDA:Multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction. Journal of Translational Medicine, 2017, 15(1):Article No. 251. DOI:10.1186/s12967-017-1340-3.
[27] Chen X, Yan C C, Zhang X et al. WBSMDA:Within and between score for MiRNA-disease association prediction. Scientific Reports, 2016, 6(1):Article No. 21106. DOI:10.1038/srep21106.
[28] You Z, Huang Z A, Zhu Z X et al. PBMDA:A novel and effective path-based computational model for miRNAdisease association prediction. PLoS Computational Biology, 2017, 13(3):Article No. e1005455. DOI:10.1371/journal.pcbi.1005455.
[29] Claude P, Gardès J. Prediction of miRNA-disease associations with a vector space mode. Scientific Reports, 2016, 6(1):Article No. 27036. DOI:10.1038/srep27036.
[30] Chen X, Huang L. LRSSLMDA:Laplacian regularized sparse subspace learning for MiRNA-disease association prediction. PLoS Computational Biology, 2017, 13(12):Article No. e1005912. DOI:10.1371/journal.pcbi.1005912.
[31] Luo J, Xiao Q. A novel approach for predicting microRNAdisease associations by unbalanced bi-random walk on heterogeneous network. Journal of Biomedical Informatics, 2017, 66:194-203. DOI:10.1016/j.jbi.2017.01.008.
[32] Chen X, Xie D, Wang L et al. BNPMDA:Bipartite network projection for MiRNA-disease association prediction. Bioinformatics, 2018, 34(18):3178-3186. DOI:10.1093/bioinformatics/bty333.
[33] Chen X, Wang L, Qu J, Guan N N, Li J Q. Predicting miRNA-disease association based on inductive matrix completion. Bioinformatics, 2018, 34(24):4256-4265. DOI:10.1093/bioinformatics/bty503.
[34] Chen X, Yin J, Qu J, Huang L. MDHGI:Matrix decomposition and heterogeneous graph inference for miRNAdisease association prediction. PLoS Computational Biology, 2018, 14(8):Article No. e1006418. DOI:10.1371/journal.pcbi.1006418.
[35] Zhao Y, Chen X, Yin J. Adaptive boosting-based computational model for predicting potential miRNA-disease associations. Bioinformatics, 2019, 35(22):4730-4738. DOI:10.1093/bioinformatics/btz297.
[36] Wang L, You Z H, Chen X et al. LMTRDA:Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities. PLoS Computational Biology, 2019, 15(3):Article No. e1006865. DOI:10.1371/journal.pcbi.1006865.
[37] Ezzat A, Zhao P, Wu M et al. Drug-target interaction prediction with graph regularized matrix factorization. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2017, 14(3):646-656. DOI:10.1109/TCBB.2016.2530062.
[38] Yan C, Duan G H, Wu F X et al. BRWMDA:Predicting microbe-disease associations based on similarities and birandom walk on disease and microbe networks. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2020, 17(5):1595-1604. DOI:10.1109/TCBB.2019.2907626.
[39] Chen X, Yan G Y. Semi-supervised learning for potential human microRNA-disease associations inference. Scientific Reports, 2014, 4(1):Article No. 5501. DOI:10.1038/srep05501.
[40] Sun Q, Zhao X, Liu X et al. miR-146a functions as a tumor suppressor in prostate cancer by targeting Rac1. The Prostate, 2014, 74(16):1613-1621. DOI:10.1002/pros.22878.
[41] Wang B S, Liu Z, Xu W X et al. Functional polymorphisms in microRNAs and susceptibility to liver cancer:A meta-analysis and meta-regression. Genetics and Molecular Research, 2014, 13(3):5426-5440. DOI:10.4238/2014.July.24.22.
[42] Guo H, Liu H, Mitchelson K et al. MicroRNAs-372/373 promote the expression of hepatitis B virus through the targeting of nuclear factor I/B. Hepatology, 2011, 54(3):808-819. DOI:10.1002/hep.24441.
[43] Shi W, Zhang Z, Yang B et al. Overexpression of microRNA let-7 correlates with disease progression and poor prognosis in hepatocellular carcinoma. Medicine, 2017, 96(32):Article No. e7764. DOI:10.1097/MD.0000000000007764.
[44] Karakatsanis A, Papaconstantinou I, Gazouli M et al. Expression of microRNAs, miR-21, miR-31, miR-122, miR-145, miR-146a, miR-200c, miR-221, miR-222, and miR-223 in patients with hepatocellular carcinoma or intrahepatic cholangioca rcinoma and its prognostic significance. Molecular Carcinogenesis, 2013, 52(4):297-303. DOI:10.1002/MC.21864.
[45] Kwanhian W, Lenze D, Alles J et al. MicroRNA-142 is mutated in about 20% of diffuse large B-cell lymphoma. Cancer Medicine, 2012, 1(2):141-155. DOI:10.1002/cam4.29.
No related articles found!
Full text



[1] Zhou Di;. A Recovery Technique for Distributed Communicating Process Systems[J]. , 1986, 1(2): 34 -43 .
[2] Chen Shihua;. On the Structure of Finite Automata of Which M Is an(Weak)Inverse with Delay τ[J]. , 1986, 1(2): 54 -59 .
[3] Wang Jianchao; Wei Daozheng;. An Effective Test Generation Algorithm for Combinational Circuits[J]. , 1986, 1(4): 1 -16 .
[4] Chen Zhaoxiong; Gao Qingshi;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[5] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[6] Zheng Guoliang; Li Hui;. The Design and Implementation of the Syntax-Directed Editor Generator(SEG)[J]. , 1986, 1(4): 39 -48 .
[7] Huang Xuedong; Cai Lianhong; Fang Ditang; Chi Bianjin; Zhou Li; Jiang Li;. A Computer System for Chinese Character Speech Input[J]. , 1986, 1(4): 75 -83 .
[8] Xu Xiaoshu;. Simplification of Multivalued Sequential SULM Network by Using Cascade Decomposition[J]. , 1986, 1(4): 84 -95 .
[9] Tang Tonggao; Zhao Zhaokeng;. Stack Method in Program Semantics[J]. , 1987, 2(1): 51 -63 .
[10] Zhong Renbao; Xing Lin; Ren Zhaoyang;. An Interactive System SDI on Microcomputer[J]. , 1987, 2(1): 64 -71 .

ISSN 1000-9000(Print)

CN 11-2296/TP

Editorial Board
Author Guidelines
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
  Copyright ©2015 JCST, All Rights Reserved