Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

Abstract

Numerous studies have demonstrated that human microRNAs (miRNAs) and diseases are associated and studies on the microRNA-disease association (MDA) have been conducted. We developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning (HSIC-MKL) to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases, and improve the model effect. We constructed three kernels in miRNA and disease space and conducted kernel fusion using HSIC-MKL. Link propagation uses matrix factorization and matrix approximation to effectively reduce computation and time costs. The results of the experiment show that the approach we proposed has a good effect, and, in some respects, exceeds what existing models can do.

Graphical abstract

Keywords

human miRNA-disease association / multiple kernel learning / link propagation / miRNA similarity / disease similarity

References

[1] Shi H, Zhang G, Zhou M, Cheng L, Yang H, Wang J, Sun J, Wang Z . Integration of multiple genomic and phenotype data to infer novel miRNA-disease associations. PLoS One, 2016, 11( 2): e0148521

[2] Carthew R W, Sontheimer E J . Origins and mechanisms of miRNAs and siRNAs. Cell, 2009, 136( 4): 642–655

[3] Peng Y, Liu Y, Chen X . Bioinformatics analysis reveals functions of MicroRNAs in rice under the drought stress. Current Bioinformatics, 2020, 15( 8): 927–936

[4] Roehle A, Hoefig K P, Repsilber D, Thorns C, Ziepert M, Wesche K O, Thiere M, Loeffler M, Klapper W, Pfreundschuh M, Matolcsy A, Bernd H W, Reiniger L, Merz H, Feller A C . MicroRNA signatures characterize diffuse large B‐cell lymphomas and follicular lymphomas. British Journal of Haematology, 2008, 142( 5): 732–744

[5] Cogswell J P, Ward J, Taylor I A, Waters M, Shi Y, Cannon B, Kelnar K, Kemppainen J, Brown D, Chen C, Prinjha R K, Richardson J C, Saunders A M, Roses A D, Richards C A . Identification of miRNA changes in Alzheimer's disease brain and CSF yields putative biomarkers and insights into disease pathways. Journal of Alzheimer's Disease, 2008, 14( 1): 27–41

[6] Caporali A, Meloni M, Völlenkle C, Bonci D, Sala-Newby G B, Addis R, Spinetti G, Losa S, Masson R, Baker A H, Agami R, Le Sage C, Condorelli G, Madeddu P, Martelli F, Emanueli C . Deregulation of microRNA-503 contributes to diabetes mellitus−induced impairment of endothelial function and reparative angiogenesis after limb ischemia. Circulation, 2011, 123( 3): 282–291

[7] Hu Y, Zhang Y, Zhang H, Gao S, Wang L, Wang T, Han Z, Sun B, Liu G. Cognitive performance protects against Alzheimer’s disease independently of educational attainment and intelligence. Molecular Psychiatry, 2022, 27(10): 4297−4306

[8] Anonymous. 2021 Alzheimer’s disease facts and figures. Alzheimer’s & Dement, 2021, 17(3): 327−406

[9] Hu Y, Sun J, Zhang Y, Zhang H, Gao S, Wang T, Han Z, Wang L, Sun B L, Liu G . rs1990622 variant associates with Alzheimer's disease and regulates TMEM106B expression in human brain tissues. BMC Medicine, 2021, 19( 1): 11

[10] Hu Y, Zhang H, Liu B, Gao S, Wang T, Han Z, International Genomics of Alzheimer’s Project (IGAP), Ji X, Liu G . rs34331204 regulates TSPAN13 expression and contributes to Alzheimer's disease with sex differences. Brain, 2020, 143( 11): e95

[11] Bhaumik D, Scott G K, Schokrpur S, Patil C K, Campisi J, Benz C C . Expression of microRNA-146 suppresses NF-κB activity with reduction of metastatic potential in breast cancer cells. Oncogene, 2008, 27( 42): 5643–5647

[12] Wang N, Li Y, Liu S, Gao L, Liu C, Bao X, Xue P . Analysis and validation of differentially expressed MicroRNAs with their target genes involved in GLP-1RA facilitated osteogenesis. Current Bioinformatics, 2021, 16( 7): 928–942

[13] Hu Y, Qiu S, Cheng L . Integration of multiple-Omics data to analyze the population-specific differences for coronary artery disease. Computational and Mathematical Methods in Medicine, 2021, 2021: 7036592

[14] Hu Y, Zhang Y, Zhang H, Gao S, Wang L, Wang T, Han Z, International Genomics of Alzheimer's Project (IGAP), Liu G . Mendelian randomization highlights causal association between genetically increased C-reactive protein levels and reduced Alzheimer's disease risk. Alzheimer’s & Dement, 2022, 18( 10): 2003–2006

[15] Tang W, Wan S, Yang Z, Teschendorff A E, Zou Q . Tumor origin detection with tissue-specific miRNA and DNA methylation markers. Bioinformatics, 2018, 34( 3): 398–406

[16] Sarkar J P, Saha I, Sarkar A, Maulik U . Machine learning integrated ensemble of feature selection methods followed by survival analysis for predicting breast cancer subtype specific miRNA biomarkers. Computers in Biology and Medicine, 2021, 131: 104244

[17] Zhu Q, Fan Y, Pan X . Fusing multiple biological networks to effectively predict miRNA-disease associations. Current Bioinformatics, 2021, 16( 3): 371–384

[18] Zhang Y, Duan G, Yan C, Yi H, Wu F X, Wang J . MDAPlatform: a component-based platform for constructing and assessing miRNA-disease association prediction methods. Current Bioinformatics, 2021, 16( 5): 710–721

[19] Chen X, Zhu C C, Yin J . Ensemble of decision tree reveals potential miRNA-disease associations. PLoS Computational Biology, 2019, 15( 7): e1007209

[20] Fu H, Huang F, Liu X, Qiu Y, Zhang W . MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks. Bioinformatics, 2022, 38( 2): 426–434

[21] Zhang G, Li M, Deng H, Xu X, Liu X, Zhang W . SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations. Briefings in Bioinformatics, 2022, 23( 1): bbab464

[22] Huang F, Yue X, Xiong Z, Yu Z, Liu S, Zhang W . Tensor decomposition with relational constraints for predicting multiple types of microRNA-disease associations. Briefings in Bioinformatics, 2021, 22( 3): bbaa140

[23] Lu X, Gao Y, Zhu Z, Ding L, Wang X, Liu F, Li J . A constrained probabilistic matrix decomposition method for predicting miRNA-disease associations. Current Bioinformatics, 2021, 16( 4): 524–533

[24] Lan W, Dong Y, Chen Q, Liu J, Wang J, Chen Y P P, Pan S . IGNSCDA: predicting CircRNA-disease associations based on improved graph convolutional network and negative sampling. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022, 19( 6): 3530–3538

[25] Peng W, Che Z, Dai W, Wei S, Lan W. Predicting miRNA-disease associations from miRNA-gene-disease heterogeneous network with multi-relational graph convolutional network model. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022, doi: 10.1109/TCBB.2022.3187739

[26] Chen X, Yan C, Zhang X, You Z H, Deng L, Liu Y, Zhang Y, Dai Q . WBSMDA: within and between score for MiRNA-disease association prediction. Scientific Reports, 2016, 6( 1): 21106

[27] Chen X, Yin J, Qu J, Huang L . MDHGI: matrix decomposition and heterogeneous graph inference for miRNA-disease association prediction. PLoS Computational Biology, 2018, 14( 8): e1006418

[28] Ding Y, Jiang L, Tang J, Guo F . Identification of human microRNA-disease association via hypergraph embedded bipartite local model. Computational Biology and Chemistry, 2020, 89: 107369

[29] 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

[30] 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

[31] Fu L, Peng Q . A deep ensemble model to predict miRNA-disease association. Scientific Reports, 2017, 7( 1): 14482

[32] Zeng X, Ding N, Rodríguez-Patón A, Zou Q . Probability-based collaborative filtering model for predicting gene−disease associations. BMC Medical Genomics, 2017, 10( S5): 76

[33] Zeng X, Liu L, Lü L Y, Zou Q . Prediction of potential disease-associated microRNAs using structural perturbation method. Bioinformatics, 2018, 34( 14): 2425–2432

[34] Zeng X, Wang W, Deng G, Bing J, Zou Q . Prediction of potential disease-associated MicroRNAs by using neural networks. Molecular Therapy Nucleic Acids, 2019, 16: 566–575

[35] Chen X, Liu M X, Yan G Y . RWRMDA: predicting novel human microRNA−disease associations. Molecular BioSystems, 2012, 8( 10): 2792–2798

[36] Van Laarhoven T, Nabuurs S B, Marchiori E . Gaussian interaction profile kernels for predicting drug−target interaction. Bioinformatics, 2011, 27( 21): 3036–3043

[37] Gu C, Liao B, Li X, Li K . Network consistency projection for human miRNA-disease associations inference. Scientific Reports, 2016, 6: 36054

[38] Tiwari P, Dehdashti S, Obeid A K, Marttinen P, Bruza P . Kernel method based on non-linear coherent states in quantum feature space. Journal of Physics A: Mathematical and Theoretical, 2022, 55( 35): 355301

[39] 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. Nucleic Acids Research, 2014, 42( D1): D1070–D1074

[40] Chen X, Li T H, Zhao Y, Wang C C, Zhu C C . Deep-belief network for predicting potential miRNA-disease associations. Briefings in Bioinformatics, 2021, 22( 3): bbaa186

[41] Wang C C, Li T H, Huang L, Chen X . Prediction of potential miRNA−disease associations based on stacked autoencoder. Briefings in Bioinformatics, 2022, 23( 2): bbac021

[42] Kozomara A, Griffiths-Jones S . miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Research, 2014, 42( D1): D68–D73

[43] 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): 1644–1650

[44] Zhu C C, Wang C C, Zhao Y, Zuo M, Chen X . Identification of miRNA−disease associations via multiple information integration with Bayesian ranking. Briefings in Bioinformatics, 2021, 22( 6): bbab302

[45] Zhao Y, Chen X, Yin J . Adaptive boosting-based computational model for predicting potential miRNA-disease associations. Bioinformatics, 2019, 35( 22): 4730–4738

[46] Lowe H J, Barnett G O . Understanding and using the medical subject headings (MeSH) vocabulary to perform literature searches. JAMA, 1994, 271( 14): 1103–1108

[47] Luo J, Xiao Q, Liang C, Ding P . Predicting MicroRNA-disease associations using Kronecker regularized least squares based on heterogeneous omics data. IEEE Access, 2017, 5: 2503–2513

[48] Lan W, Wang J, Li M, Liu J, Wu F X, Pan Y . Predicting microRNA-disease associations based on improved microRNA and disease similarities. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018, 15( 6): 1774–1782

[49] Lee I, Blom U M, Wang P I, Shim J E, Marcotte E M . Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Research, 2011, 21( 7): 1109–1121

[50] Cheng L, Wang G, Li J, Zhang T, Xu P, Wang Y . SIDD: a semantically integrated database towards a global view of human disease. PLoS One, 2013, 8( 10): e75504

[51] Gretton A, Bousquet O, Smola A, Schölkopf B . Measuring statistical dependence with Hilbert-Schmidt norms. In: Proceedings of the 16th International Conference on Algorithmic Learning Theory, 2005, 63–77

[52] Wang T, Li W . Kernel learning and optimization with Hilbert−Schmidt independence criterion. International Journal of Machine Learning and Cybernetics, 2018, 9( 10): 1707–1717

[53] Xuan J, Lu J, Yan Z, Zhang G . Bayesian deep reinforcement learning via deep kernel learning. International Journal of Computational Intelligence Systems, 2018, 12( 1): 164–171

[54] Wang T, Lu J, Zhang G . Two-stage fuzzy multiple kernel learning based on Hilbert−Schmidt independence criterion. IEEE Transactions on Fuzzy Systems, 2018, 26( 6): 3703–3714

[55] Gönen M, Alpaydın E . Multiple kernel learning algorithms. The Journal of Machine Learning Research, 2011, 12: 2211–2268

[56] Jiang L, Ding Y, Tang J, Guo F . MDA-SKF: similarity kernel fusion for accurately discovering miRNA-disease association. Frontiers in Genetics, 2018, 9: 618

[57] Zhou D, Bousquet O, Lal T N, Weston J, Schölkopf B . Learning with local and global consistency. In: Proceedings of the 16th International Conference on Neural Information Processing Systems, 2003, 321–328

[58] Zhu X, Ghahramani Z, Lafferty J . Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), 2003, 912–919

[59] Raymond R, Kashima H . Fast and scalable algorithms for semi-supervised link prediction on static and dynamic graphs. In: Proceedings of 2010 European Conference on Machine Learning and Knowledge Discovery in Databases, 2010, 131–147

[60] Laub A J. Matrix Analysis for Scientists and Engineers. Philadelphia: SIAM, 2005

[61] Kashima H, Kato T, Yamanishi Y, Sugiyama M, Tsuda K . Link propagation: a fast semi-supervised learning algorithm for link prediction. In: Proceedings of the 9th SIAM International Conference on Data Mining, 2009, 1093–1104

[62] Golub G H, Hoffman A, Stewart G W. A generalization of the Eckart-Young-Mirsky matrix approximation theorem. Linear Algebra and its Applications, 1987, 88–89: 317-327

[63] Bishop C M, Nasrabadi N M. Pattern Recognition and Machine Learning. New York: Springer, 2006

[64] Vishwanathan S V N, Borgwardt K M, Schraudolph N N. Fast computation of graph kernels. In: Proceedings of the 19th International Conference on Neural Information Processing Systems. 2006

[65] Jiang L, Xiao Y, Ding Y, Tang J, Guo F . FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association. BMC Genomics, 2018, 19( S10): 911

[66] Ding Y, Tiwari P, Zou Q, Guo F, Pandey H M . C-loss based higher order fuzzy inference systems for identifying DNA N4-methylcytosine sites. IEEE Transactions on Fuzzy Systems, 2022, 30( 11): 4754–4765

[67] Chen X, Xie D, Wang L, Zhao Q, You Z H, Liu H . BNPMDA: bipartite network projection for MiRNA−disease association prediction. Bioinformatics, 2018, 34( 18): 3178–3186

[68] Cristianini N, Shawe-Taylor J, Elisseeff A, Kandola J . On kernel-target alignment. In: Proceedings of the 14th International Conference on Neural Information Processing Systems, 2001, 367–373

[69] Cortes C, Mohri M, Rostamizadeh A . Algorithms for learning kernels based on centered alignment. The Journal of Machine Learning Research, 2012, 13( 1): 795–828

[70] Lu Y, Wang L, Lu J, Yang J, Shen C . Multiple kernel clustering based on centered kernel alignment. Pattern Recognition, 2014, 47( 11): 3656–3664

[71] Hu J, Li Y, Zhang M, Yang X, Shen H B, Yu D J . Predicting protein-DNA binding residues by weightedly combining sequence-based features and boosting multiple SVMs. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017, 14( 6): 1389–1398

[72] Wang H, Tang J, Ding Y, Guo F . Exploring associations of non-coding RNAs in human diseases via three-matrix factorization with hypergraph-regular terms on center kernel alignment. Briefings in Bioinformatics, 2021, 22( 5): bbaa409

[73] Chen X, Huang L . LRSSLMDA: laplacian regularized sparse subspace learning for MiRNA-disease association prediction. PLoS Computational Biology, 2017, 13( 12): e1005912

[74] You Z H, Huang Z A, Zhu Z, Yan G Y, Li Z W, Wen Z, Chen X . PBMDA: a novel and effective path-based computational model for miRNA-disease association prediction. PLoS Computational Biology, 2017, 13( 3): e1005455

[75] Li J Q, Rong Z H, Chen X, Yan G Y, You Z H . MCMDA: matrix completion for MiRNA-disease association prediction. Oncotarget, 2017, 8( 13): 21187–21199

[76] Chen X, Yan G Y . Semi-supervised learning for potential human microRNA-disease associations inference. Scientific Reports, 2014, 4: 5501

[77] Xuan P, Han K, Guo M, Guo Y, Li J, Ding J, Liu Y, Dai Q, Li J, Teng Z, Huang Y . Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PLoS One, 2013, 8( 8): e70204

[78] Chen X, Xie D, Zhao Q, You Z H . MicroRNAs and complex diseases: from experimental results to computational models. Briefings in Bioinformatics, 2019, 20( 2): 515–539

RIGHTS & PERMISSIONS

Higher Education Press