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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

Fusing Multiple Biological Networks to Effectively Predict miRNA-disease Associations

Author(s): Qingqi Zhu, Yongxian Fan* and Xiaoyong Pan

Volume 16, Issue 3, 2021

Published on: 15 July, 2020

Page: [371 - 384] Pages: 14

DOI: 10.2174/1574893615999200715165335

Price: $65

Abstract

Background: MicroRNAs (miRNAs) are a class of endogenous non-coding RNAs with about 22 nucleotides, and they play a significant role in a variety of complex biological processes. Many researches have shown that miRNAs are closely related to human diseases. Although the biological experiments are reliable in identifying miRNA-disease associations, they are timeconsuming and costly.

Objective: Thus, computational methods are urgently needed to effectively predict miRNA-disease associations.

Methods: In this paper, we proposed a novel method, BIRWMDA, based on a bi-random walk model to predict miRNA-disease associations. Specifically, in BIRWMDA, the similarity network fusion algorithm is used to combine the multiple similarity matrices to obtain a miRNA-miRNA similarity matrix and a disease-disease similarity matrix, then the miRNA-disease associations were predicted by the bi-random walk model.

Results: To evaluate the performance of BIRWMDA, we ran the leave-one-out cross-validation and 5-fold cross-validation, and their corresponding AUCs were 0.9303 and 0.9223 ± 0.00067, respectively. To further demonstrate the effectiveness of the BIRWMDA, from the perspective of exploring disease-related miRNAs, we conducted three case studies of breast neoplasms, prostate neoplasms and gastric neoplasms, where 48, 50 and 50 out of the top 50 predicted miRNAs were confirmed by literature, respectively. From the perspective of exploring miRNA-related diseases, we conducted two case studies of hsa-mir-21 and hsa-mir-155, where 7 and 5 out of the top 10 predicted diseases were confirmed by literatures, respectively.

Conclusion: The fusion of multiple biological networks could effectively predict miRNA-diseases associations. We expected BIRWMDA to serve as a biological tool for mining potential miRNAdisease associations.

Keywords: MiRNA, disease, miRNA-disease associations, similarity network fusion, bi-random walk, biological networks.

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[1]
Ambros V. The functions of animal microRNAs. Nature 2004; 431(7006): 350-5.
[http://dx.doi.org/10.1038/nature02871] [PMID: 15372042]
[2]
Ribeiro AO, Schoof CR, Izzotti A, Pereira LV, Vasques LR. MicroRNAs: modulators of cell identity, and their applications in tissue engineering. MicroRNA 2014; 3(1): 45-53.
[http://dx.doi.org/10.2174/2211536603666140522003539] [PMID: 25069512]
[3]
Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function . Cell 2004; 116(2): 281-97..
[http://dx.doi.org/10.1016/S0092-8674(04)00045-5] [PMID: 14744438]
[4]
Cheng AM, Byrom MW, Shelton J, Ford LP. Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Res 2005; 33(4): 1290-7.
[http://dx.doi.org/10.1093/nar/gki200] [PMID: 15741182]
[5]
Xu P, Guo M, Hay BA. MicroRNAs and the regulation of cell death. Trends Genet 2004; 20(12): 617-24.
[http://dx.doi.org/10.1016/j.tig.2004.09.010] [PMID: 15522457]
[6]
Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell 2009; 136(2): 215-33.
[http://dx.doi.org/10.1016/j.cell.2009.01.002] [PMID: 19167326]
[7]
Miska EA. How microRNAs control cell division, differentiation and death. Curr Opin Genet Dev 2005; 15(5): 563-8.
[http://dx.doi.org/10.1016/j.gde.2005.08.005] [PMID: 16099643]
[8]
Karp X, Ambros V. Developmental biology. Encountering microRNAs in cell fate signaling. Science 2005; 310(5752): 1288-9.
[http://dx.doi.org/10.1126/science.1121566] [PMID: 16311325]
[9]
Gruszka R, Zakrzewska M. The oncogenic relevance of miR-17-92 cluster and its paralogous miR-106b-25 and miR-106a-363 clusters in brain tumors. Int J Mol Sci 2018; 19(3): 879.
[http://dx.doi.org/10.3390/ijms19030879] [PMID: 29547527]
[10]
Wang M, Ren D, Guo W, et al. Loss of miR-100 enhances migration, invasion, epithelial-mesenchymal transition and stemness properties in prostate cancer cells through targeting Argonaute 2. Int J Oncol 2014; 45(1): 362-72.
[http://dx.doi.org/10.3892/ijo.2014.2413] [PMID: 24805183]
[11]
Shrestha S, Hsu SD, Huang WY, et al. A systematic review of microRNA expression profiling studies in human gastric cancer. Cancer Med 2014; 3(4): 878-88.
[http://dx.doi.org/10.1002/cam4.246] [PMID: 24902858]
[12]
Pacholewska A, Kraft MF, Gerber V, Jagannathan V. Differential expression of serum MicroRNAs supports CD4+ T cell differentiation into Th2/Th17 cells in severe equine asthma. Genes (Basel) 2017; 8(12): 383.
[http://dx.doi.org/10.3390/genes8120383] [PMID: 29231896]
[13]
Guo Z, Wu R, Gong J, et al. Altered microRNA expression in inflamed and non-inflamed terminal ileal mucosa of adult patients with active Crohn’s disease. J Gastroenterol Hepatol 2015; 30(1): 109-16.
[http://dx.doi.org/10.1111/jgh.12644] [PMID: 24910152]
[14]
Alvarez-Garcia I, Miska EA. MicroRNA functions in animal development and human disease. Development 2005; 132(21): 4653-62.
[http://dx.doi.org/10.1242/dev.02073] [PMID: 16224045]
[15]
Ding P, Luo J, Xiao Q, Chen X. A path-based measurement for human miRNA functional similarities using miRNA-disease associations. Sci Rep 2016; 6: 32533.
[http://dx.doi.org/10.1038/srep32533] [PMID: 27585796]
[16]
Zeng X, Zhang X, Zou Q. Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Brief Bioinform 2016; 17(2): 193-203.
[http://dx.doi.org/10.1093/bib/bbv033] [PMID: 26059461]
[17]
Jiang Q, Hao Y, Wang G, et al. Prioritization of disease microRNAs through a human phenome-microRNAome network. BMC Syst Biol 2010; 4(1)(Suppl. 1): S2.
[http://dx.doi.org/10.1186/1752-0509-4-S1-S2] [PMID: 20522252]
[18]
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)e70204
[http://dx.doi.org/10.1371/journal.pone.0070204] [PMID: 23950912]
[19]
Chen X, Liu MX, Yan GY. RWRMDA: predicting novel human microRNA-disease associations. Mol Biosyst 2012; 8(10): 2792-8.
[http://dx.doi.org/10.1039/c2mb25180a] [PMID: 22875290]
[20]
Shi H, Xu J, Zhang G, et al. Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes. BMC Syst Biol 2013; 7(1): 101.
[http://dx.doi.org/10.1186/1752-0509-7-101] [PMID: 24103777]
[21]
Liu YS, Zeng XX, He ZY, et al. .Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources IEEE/ACM Trans Comput Biol Bioinform 2017; 14(4): 905-15..
[http://dx.doi.org/10.1109/TCBB.2016.2550432]
[22]
You ZH, Huang ZA, Zhu Z, et al. PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction. PLOS Comput Biol 2017; 13(3)e1005455
[http://dx.doi.org/10.1371/journal.pcbi.1005455] [PMID: 28339468]
[23]
Li JQ, Rong ZH, Chen X, Yan GY, You ZH. MCMDA: matrix completion for MiRNA-disease association prediction. Oncotarget 2017; 8(13): 21187-99.
[http://dx.doi.org/10.18632/oncotarget.15061] [PMID: 28177900]
[24]
Xu J, Li CX, Lv JY, et al. Prioritizing candidate disease miRNAs by topological features in the miRNA target-dysregulated network: case study of prostate cancer. Mol Cancer Ther 2011; 10(10): 1857-66.
[http://dx.doi.org/10.1158/1535-7163.MCT-11-0055] [PMID: 21768329]
[25]
Jiang Q, Wang G, Jin S, Li Y, Wang Y. Predicting human microRNA-disease associations based on support vector machine. Int J Data Min Bioinform 2013; 8(3): 282-93.
[http://dx.doi.org/10.1504/IJDMB.2013.056078] [PMID: 24417022]
[26]
Chen X, Yan GY. Semi-supervised learning for potential human microRNA-disease associations inference. Sci Rep 2014; 4: 5501.
[http://dx.doi.org/10.1038/srep05501] [PMID: 24975600]
[27]
Luo JW, Xiao Q, Liang C, et al. Predicting microRNA-disease associations using kronecker regularized least squares based on heterogeneous omics data IEEE Access 2017; 5: 2503-13..
[http://dx.doi.org/10.1109/ACCESS.2017.2672600]
[28]
Peng J, Hui W, Li Q, et al. A learning-based framework for miRNA-disease association identification using neural networks. Bioinformatics 2019; 35(21): 4364-71.
[http://dx.doi.org/10.1093/bioinformatics/btz254] [PMID: 30977780]
[29]
Chen X, Wang CC, Yin J, You ZH. Novel human miRNA-disease association inference based on random forest. Mol Ther Nucleic Acids 2018; 13: 568-79.
[http://dx.doi.org/10.1016/j.omtn.2018.10.005] [PMID: 30439645]
[30]
Chen X, Yan CC, Zhang X, et al. WBSMDA: within and between score for miRNA-disease association prediction. Sci Rep 2016; 6: 21106.
[http://dx.doi.org/10.1038/srep21106] [PMID: 26880032]
[31]
Li Y, Qiu C, Tu J, et al. HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res 2014; 42(Database issue): D1070-4.
[http://dx.doi.org/10.1093/nar/gkt1023] [PMID: 24194601]
[32]
Bandyopadhyay S, Mitra R, Maulik U, Zhang MQ. Development of the human cancer microRNA network. Silence 2010; 1(1): 6.
[http://dx.doi.org/10.1186/1758-907X-1-6] [PMID: 20226080]
[33]
Goh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barabási AL. The human disease network. Proc Natl Acad Sci USA 2007; 104(21): 8685-90.
[http://dx.doi.org/10.1073/pnas.0701361104] [PMID: 17502601]
[34]
Wang D, Wang J, Lu M, Song F, Cui Q. Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics 2010; 26(13): 1644-50.
[http://dx.doi.org/10.1093/bioinformatics/btq241] [PMID: 20439255]
[35]
Chen X, Wang L, Qu J, Guan NN, Li JQ. Predicting miRNA-disease association based on inductive matrix completion. Bioinformatics 2018; 34(24): 4256-65.
[http://dx.doi.org/10.1093/bioinformatics/bty503] [PMID: 29939227]
[36]
Chen X, Yang JR, Guan NN, Li JQ. GRMDA: graph regression for MiRNA-disease association prediction. Front Physiol 2018; 9: 92.
[http://dx.doi.org/10.3389/fphys.2018.00092] [PMID: 29515453]
[37]
Chen X, Zhou Z, Zhao Y. ELLPMDA: Ensemble learning and link prediction for miRNA-disease association prediction. RNA Biol 2018; 15(6): 807-18.
[http://dx.doi.org/10.1080/15476286.2018.1460016] [PMID: 29619882]
[38]
Wang B, Mezlini AM, Demir F, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 2014; 11(3): 333-7.
[http://dx.doi.org/10.1038/nmeth.2810] [PMID: 24464287]
[39]
Zheng X, Wang Y, Tian K, et al. Fusing multiple protein-protein similarity networks to effectively predict lncRNA-protein interactions. BMC Bioinformatics 2017; 18(12): 420.
[http://dx.doi.org/10.1186/s12859-017-1819-1] [PMID: 29072138]
[40]
Chen QF, Lai DH, Lan W, et al. ILDMSF: inferring associations between long non-coding rna and disease based on multi-similarity fusion . IEEE/ACM Trans Comput Biol Bioinform 2019;.
[http://dx.doi.org/10.1109/TCBB.2019.2936476]
[41]
Luo H, Wang J, Li M, et al. Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm. Bioinformatics 2016; 32(17): 2664-71.
[http://dx.doi.org/10.1093/bioinformatics/btw228] [PMID: 27153662]
[42]
Sun D, Li A, Feng H, Wang M. NTSMDA: prediction of miRNA-disease associations by integrating network topological similarity. Mol Biosyst 2016; 12(7): 2224-32.
[http://dx.doi.org/10.1039/C6MB00049E] [PMID: 27153230]
[43]
Qu Y, Zhang HX, Liang C, et al. KATZMDA: prediction of mirna-disease associations based on KATZ model IEEE Access 2017; 6: 174 3943-50
[http://dx.doi.org/10.1109/ACCESS.2017.2754409]
[44]
Bao W, Jiang Z, Huang DS. Novel human microbe-disease association prediction using network consistency projection. BMC Bioinformatics 2017; 18(16): 543.
[http://dx.doi.org/10.1186/s12859-017-1968-2] [PMID: 29297304]
[45]
Li GH, Luo JW, Xiao Q, et al. Prediction of microRNA-disease associations with a Kronecker kernel matrix dimension reduction model. RSC Advances 2018; 8(8): 4377-85.
[http://dx.doi.org/10.1039/C7RA12491K]
[46]
Fan YX, Shen HB. Predicting pupylation sites in prokaryotic proteins using pseudo-amino acid composition and extreme learning machine. Neurocomputing 2014; 128: 267-72.
[http://dx.doi.org/10.1016/j.neucom.2012.11.058]
[47]
Peng LH, Yin J, Zhou L, Liu MX, Zhao Y. Human microbe-disease association prediction based on adaptive boosting. Front Microbiol 2018; 9: 2440.
[http://dx.doi.org/10.3389/fmicb.2018.02440] [PMID: 30356751]
[48]
Fan YX, Zhang Y, Shen HB. LabCaS: labeling calpain substrate cleavage sites from amino acid sequence using conditional random fields. Proteins 2013; 81(4): 622-34.
[http://dx.doi.org/10.1002/prot.24217] [PMID: 23180633]
[49]
Chen X, Wu QF, Yan GY. RKNNMDA: ranking-based KNN for miRNA-disease association prediction . RNA Biol 2017; 14(7): 952-62..
[http://dx.doi.org/10.1080/15476286.2017.1312226] [PMID: 28421868]
[50]
Huang Z, Shi J, Gao Y, et al. HMDD v3.0: a database for experimentally supported human microRNA-disease associations. Nucleic Acids Res 2019; 47(D1): D1013-7.
[http://dx.doi.org/10.1093/nar/gky1010] [PMID: 30364956]
[51]
Xie B, Ding Q, Han H, Wu D. miRCancer: a microRNA-cancer association database constructed by text mining on literature. Bioinformatics 2013; 29(5): 638-44.
[http://dx.doi.org/10.1093/bioinformatics/btt014] [PMID: 23325619]
[52]
Yang Z, Wu L, Wang A, et al. dbDEMC 2.0: updated database of differentially expressed miRNAs in human cancers. Nucleic Acids Res 2017; 45(D1): D812-8.
[http://dx.doi.org/10.1093/nar/gkw1079] [PMID: 27899556]
[53]
Network CGA. Cancer genome Atlas network. Comprehensive molecular portraits of human breast tumours. Nature 2012; 490(7418): 61-70.
[http://dx.doi.org/10.1038/nature11412] [PMID: 23000897]
[54]
Nandy SB, Arumugam A, Subramani R, et al. MicroRNA-125a influences breast cancer stem cells by targeting leukemia inhibitory factor receptor which regulates the Hippo signaling pathway. Oncotarget 2015; 6(19): 17366-78.
[http://dx.doi.org/10.18632/oncotarget.3953] [PMID: 25962054]
[55]
Jedlinski DJ, Gabrovska PN, Weinstein SR, Smith RA, Griffiths LR. Single nucleotide polymorphism in hsa-mir-196a-2 and breast cancer risk: a case control study. Twin Res Hum Genet 2011; 14(5): 417-21.
[http://dx.doi.org/10.1375/twin.14.5.417] [PMID: 21962133]
[56]
Kasivisvanathan V, Rannikko AS, Borghi M, et al. PRECISION study group collaborators. MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med 2018; 378(19): 1767-77.
[http://dx.doi.org/10.1056/NEJMoa1801993] [PMID: 29552975]
[57]
Dezhong L, Xiaoyi Z, Xianlian L, et al. miR-150 is a factor of survival in prostate cancer patients. J BUON 2015; 20(1): 173-9.
[PMID: 25778313]
[58]
Zhang YQ, Zheng TL, Zhang WD. Report of cancer incidence and mortality in China, 2012. Adv Mod Oncol Res 2018; 4(3): 1-7.
[59]
Pan X, Fan YX, Yan J, Shen HB. IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction. BMC Genomics 2016; 17(1): 582.
[http://dx.doi.org/10.1186/s12864-016-2931-8] [PMID: 27506469]
[60]
Pan X, Shen HB. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. BMC Bioinformatics 2017; 18(1): 136.
[http://dx.doi.org/10.1186/s12859-017-1561-8] [PMID: 28245811]
[61]
Pan X, Shen HB. Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks. Bioinformatics 2018; 34(20): 3427-36.
[http://dx.doi.org/10.1093/bioinformatics/bty364] [PMID: 29722865]
[62]
Park Y, Marcotte EM. Flaws in evaluation schemes for pair-input computational predictions. Nat Methods 2012; 9(12): 1134-6.
[http://dx.doi.org/10.1038/nmeth.2259] [PMID: 23223166]

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