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Current Genomics

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ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

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General Research Article

IMPMD: An Integrated Method for Predicting Potential Associations Between miRNAs and Diseases

Author(s): Meiqi Wu, Yingxi Yang, Hui Wang, Jun Ding, Huan Zhu and Yan Xu*

Volume 20, Issue 8, 2019

Page: [581 - 591] Pages: 11

DOI: 10.2174/1389202920666191023090215

Price: $65

Abstract

Background: With the rapid development of biological research, microRNAs (miRNAs) have increasingly attracted worldwide attention. The increasing biological studies and scientific experiments have proven that miRNAs are related to the occurrence and development of a large number of key biological processes which cause complex human diseases. Thus, identifying the association between miRNAs and disease is helpful to diagnose the diseases. Although some studies have found considerable associations between miRNAs and diseases, there are still a lot of associations that need to be identified. Experimental methods to uncover miRNA-disease associations are time-consuming and expensive. Therefore, effective computational methods are urgently needed to predict new associations.

Methodology: In this work, we propose an integrated method for predicting potential associations between miRNAs and diseases (IMPMD). The enhanced similarity for miRNAs is obtained by combination of functional similarity, gaussian similarity and Jaccard similarity. To diseases, it is obtained by combination of semantic similarity, gaussian similarity and Jaccard similarity. Then, we use these two enhanced similarities to construct the features and calculate cumulative score to choose robust features. Finally, the general linear regression is applied to assign weights for Support Vector Machine, K-Nearest Neighbor and Logistic Regression algorithms.

Results: IMPMD obtains AUC of 0.9386 in 10-fold cross-validation, which is better than most of the previous models. To further evaluate our model, we implement IMPMD on two types of case studies for lung cancer and breast cancer. 49 (Lung Cancer) and 50 (Breast Cancer) out of the top 50 related miRNAs are validated by experimental discoveries.

Conclusion: We built a software named IMPMD which can be freely downloaded from https:// github.com/Sunmile/IMPMD.

Keywords: miRNA, disease, miRNA-disease associations, integrated algorithm, IMPMD, computational methods.

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[1]
Ambros, V. The functions of animal microRNAs. Nature, 2004, 431(7006), 350-355.
[http://dx.doi.org/10.1038/nature02871] [PMID: 15372042]
[2]
Bartel, D.P. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 2004, 116(2), 281-297.
[http://dx.doi.org/10.1016/S0092-8674(04)00045-5] [PMID: 14744438]
[3]
Meister, G.; Tuschl, T. Mechanisms of gene silencing by double-stranded RNA. Nature, 2004, 431(7006), 343-349.
[http://dx.doi.org/10.1038/nature02873] [PMID: 15372041]
[4]
Ambros, V. MicroRNAs: tiny regulators with great potential. Cell, 2001, 107(7), 823-826.
[http://dx.doi.org/10.1016/S0092-8674(01)00616-X] [PMID: 11779458]
[5]
Chen, X.; Gong, Y.; Zhang, D.H.; You, Z.H.; Li, Z.W. DRMDA: deep representations-based miRNA-disease association prediction. J. Cell. Mol. Med., 2018, 22(1), 472-485.
[http://dx.doi.org/10.1111/jcmm.13336] [PMID: 28857494]
[6]
Lee, R.C.; Feinbaum, R.L.; Ambros, V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 1993, 75(5), 843-854.
[http://dx.doi.org/10.1016/0092-8674(93)90529-Y] [PMID: 8252621 ]
[7]
Jopling, C.L.; Yi, M.; Lancaster, A.M.; Lemon, S.M.; Sarnow, P. Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science, 2005, 309(5740), 1577-1581.
[http://dx.doi.org/10.1126/science.1113329] [PMID: 16141076]
[8]
Kozomara, A.; Griffiths-Jones, S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res., 2011, 39(Database issue), D152-D157.
[http://dx.doi.org/10.1093/nar/gkq1027] [PMID: 21037258]
[9]
Ambros, V. MicroRNA pathways in flies and worms: growth, death, fat, stress, and timing. Cell, 2003, 113(6), 673-676.
[http://dx.doi.org/10.1016/S0092-8674(03)00428-8] [PMID: 12809598]
[10]
Xu, P.; Guo, M.; Hay, B.A. MicroRNAs and the regulation of cell death. Trends Genet., 2004, 20(12), 617-624.
[http://dx.doi.org/10.1016/j.tig.2004.09.010] [PMID: 15522457]
[11]
Cheng, A.M.; Byrom, M.W.; Shelton, J.; Ford, L.P. Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Res., 2005, 33(4), 1290-1297.
[http://dx.doi.org/10.1093/nar/gki200] [PMID: 15741182]
[12]
Miska, E.A. How microRNAs control cell division, differentiation and death. Curr. Opin. Genet. Dev., 2005, 15(5), 563-568.
[http://dx.doi.org/10.1016/j.gde.2005.08.005] [PMID: 16099643]
[13]
Taganov, K.D.; Boldin, M.P.; Chang, K.J.; Baltimore, D. NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc. Natl. Acad. Sci. USA, 2006, 103(33), 12481-12486.
[http://dx.doi.org/10.1073/pnas.0605298103] [PMID: 16885212]
[14]
Calin, G.A.; Dumitru, C.D.; Shimizu, M.; Bichi, R.; Zupo, S.; Noch, E.; Aldler, H.; Rattan, S.; Keating, M.; Rai, K.; Rassenti, L.; Kipps, T.; Negrini, M.; Bullrich, F.; Croce, C.M. Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc. Natl. Acad. Sci. USA, 2002, 99(24), 15524-15529.
[http://dx.doi.org/10.1073/pnas.242606799] [PMID: 12434020]
[15]
Chen, X.; Xie, D.; Zhao, Q.; You, Z.H. MicroRNAs and complex diseases: from experimental results to computational models. Brief. Bioinform., 2019, 20(2), 515-539.
[http://dx.doi.org/10.1093/bib/bbx130] [PMID: 29045685]
[16]
Song, T.; Zhang, X.; Zhang, L.; Dong, J.; Cai, W.; Gao, J.; Hong, B. miR-708 promotes the development of bladder carcinoma via direct repression of Caspase-2. J. Cancer Res. Clin. Oncol., 2013, 139(7), 1189-1198.
[http://dx.doi.org/10.1007/s00432-013-1392-6] [PMID: 23568547]
[17]
Schulte, C.; Molz, S.; Appelbaum, S.; Karakas, M.; Ojeda, F.; Lau, D.M.; Hartmann, T.; Lackner, K.J.; Westermann, D.; Schnabel, R.B.; Blankenberg, S.; Zeller, T. miRNA-197 and miRNA-223 predict cardiovascular death in a cohort of patients with symptomatic coronary artery disease. PLoS One, 2015, 10(12)e0145930
[http://dx.doi.org/10.1371/journal.pone.0145930] [PMID: 26720041]
[18]
Bang, C.; Fiedler, J.; Thum, T. Cardiovascular importance of the microRNA-23/27/24 family. Microcirculation, 2012, 19(3), 208-214.
[http://dx.doi.org/10.1111/j.1549-8719.2011.00153.x] [PMID: 22136461]
[19]
Mohammadi-Yeganeh, S.; Paryan, M.; Mirab Samiee, S.; Soleimani, M.; Arefian, E.; Azadmanesh, K.; Mostafavi, E.; Mahdian, R.; Karimipoor, M. Development of a robust, low cost stem-loop real-time quantification PCR technique for miRNA expression analysis. Mol. Biol. Rep., 2013, 40(5), 3665-3674.
[http://dx.doi.org/10.1007/s11033-012-2442-x] [PMID: 23307300]
[20]
Thomson, J.M.; Parker, J.S.; Hammond, S.M. Microarray analysis of miRNA gene expression. Methods Enzymol., 2007, 427, 107-122.
[http://dx.doi.org/10.1016/S0076-6879(07)27006-5] [PMID: 17720481]
[21]
Chen, X. Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA. Sci. Rep., 2015, 5, 13186.
[http://dx.doi.org/10.1038/srep13186] [PMID: 26278472]
[22]
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.
[http://dx.doi.org/10.1093/bioinformatics/bty503] [PMID: 29939227]
[23]
Chen, X.; Yin, J.; Qu, J.; Huang, L. MDHGI: Matrix decomposition and heterogeneous graph inference for miRNA-disease association prediction. PLOS Comput. Biol., 2018, 14(8)e1006418
[http://dx.doi.org/10.1371/journal.pcbi.1006418] [PMID: 30142158]
[24]
Chen, X.; Huang, L. LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction. PLOS Comput. Biol., 2017, 13(12)e1005912
[http://dx.doi.org/10.1371/journal.pcbi.1005912] [PMID: 29253885]
[25]
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 Comput. Biol., 2017, 13(3)e1005455
[http://dx.doi.org/10.1371/journal.pcbi.1005455] [PMID: 28339468]
[26]
Chen, X.; Wang, L.Y.; Huang, L. NDAMDA: Network distance analysis for MiRNA-disease association prediction. J. Cell. Mol. Med., 2018, 22(5), 2884-2895.
[http://dx.doi.org/10.1111/jcmm.13583] [PMID: 29532987]
[27]
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.
[http://dx.doi.org/10.1093/bioinformatics/bty333] [PMID: 29701758]
[28]
Chen, X.; Huang, L.; Xie, D.; Zhao, Q. EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction. Cell Death Dis., 2018, 9(1), 3.
[http://dx.doi.org/10.1038/s41419-017-0003-x] [PMID: 29305594]
[29]
Zhao, Y.; Chen, X.; Yin, J. Adaptive boosting-based computational model for predicting potential miRNA-disease associations. Bioinformatics, 2019, 35(22), 4730-4738.
[http://dx.doi.org/10.1093/bioinformatics/btz297] [PMID: 31038664]
[30]
Jiang, Q.; Hao, Y.; Wang, G.; Juan, L.; Zhang, T.; Teng, M.; Liu, Y.; Wang, Y. Prioritization of disease microRNAs through a human phenome-microRNAome network. BMC Syst. Biol., 2010, 4(Suppl. 1), S2.
[http://dx.doi.org/10.1186/1752-0509-4-S1-S2] [PMID: 20522252]
[31]
Shi, H.; Xu, J.; Zhang, G.; Xu, L.; Li, C.; Wang, L.; Zhao, Z.; Jiang, W.; Guo, Z.; Li, X. Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes. BMC Syst. Biol., 2013, 7, 101.
[http://dx.doi.org/10.1186/1752-0509-7-101] [PMID: 24103777]
[32]
Chen, X.; Liu, M.X.; Yan, G.Y. RWRMDA: predicting novel human microRNA-disease associations. Mol. Biosyst., 2012, 8(10), 2792-2798.
[http://dx.doi.org/10.1039/c2mb25180a] [PMID: 22875290]
[33]
Xuan, P.; Han, K.; Guo, Y.; Li, J.; Li, X.; Zhong, Y.; Zhang, Z.; Ding, J. Prediction of potential disease-associated microRNAs based on random walk. Bioinformatics, 2015, 31(11), 1805-1815.
[http://dx.doi.org/10.1093/bioinformatics/btv039] [PMID: 25618864]
[34]
Zhao, Y.; Chen, X.; Yin, J. A novel computational method for the identification of potential miRNA-disease association based on symmetric non-negative matrix factorization and kronecker regularized least square. Front. Genet., 2018, 9, 324.
[http://dx.doi.org/10.3389/fgene.2018.00324] [PMID: 30186308]
[35]
Chen, X.; Yan, G.Y. Semi-supervised learning for potential human microRNA-disease associations inference. Sci. Rep., 2014, 4, 5501.
[http://dx.doi.org/10.1038/srep05501] [PMID: 24975600]
[36]
Chen, X.; Zhou, Z.; Zhao, Y. ELLPMDA: Ensemble learning and link prediction for miRNA-disease association prediction. RNA Biol., 2018, 15(6), 807-818.
[http://dx.doi.org/10.1080/15476286.2018.1460016] [PMID: 29619882]
[37]
Chen, X.; Cheng, J.Y.; Yin, J. Predicting microRNA-disease associations using bipartite local models and hubness-aware regression. RNA Biol., 2018, 15(9), 1192-1205.
[http://dx.doi.org/10.1080/15476286.2018.1517010] [PMID: 30196756]
[38]
Niu, Y.W.; Wang, G.H.; Yan, G.Y.; Chen, X. Integrating random walk and binary regression to identify novel miRNA-disease association. BMC Bioinformatics, 2019, 20(1), 59.
[http://dx.doi.org/10.1186/s12859-019-2640-9] [PMID: 30691413]
[39]
Pasquier, C.; Gardès, J. Prediction of miRNA-disease associations with a vector space model. Sci. Rep., 2016, 6, 27036.
[http://dx.doi.org/10.1038/srep27036] [PMID: 27246786]
[40]
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.
[http://dx.doi.org/10.1093/bioinformatics/btq241] [PMID: 20439255]
[41]
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
[http://dx.doi.org/10.1371/journal.pone.0070204] [PMID: 23950912]
[42]
Wang, C.C.; Chen, X.; Yin, J.; Qu, J. An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy. RNA Biol., 2019, 16(3), 257-269.
[http://dx.doi.org/10.1080/15476286.2019.1568820] [PMID: 30646823]
[43]
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.
[http://dx.doi.org/10.18632/oncotarget.15061] [PMID: 28177900]
[44]
Yang, Z.; Wu, L.; Wang, A.; Tang, W.; Zhao, Y.; Zhao, H.; Teschendorff, A.E. dbDEMC 2.0: updated database of differentially expressed miRNAs in human cancers. Nucleic Acids Res., 2017, 45(D1), D812-D818.
[http://dx.doi.org/10.1093/nar/gkw1079] [PMID: 27899556]
[45]
Ruepp, A.; Kowarsch, A.; Schmidl, D.; Buggenthin, F.; Brauner, B.; Dunger, I.; Fobo, G.; Frishman, G.; Montrone, C.; Theis, F.J.; Phenomi, R. PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes. Genome Biol., 2010, 11(1), R6.
[http://dx.doi.org/10.1186/gb-2010-11-1-r6] [PMID: 20089154]
[46]
Das, S.S.; Saha, P.; Chakravorty, N. miRwayDB: a database for experimentally validated mi-croRNA-pathway associations in pathophysiological conditions. Database (Oxford), 2018.
[47]
Xue, Z.; Wen, J.; Chu, X.; Xue, X. A microRNA gene signature for identification of lung cancer. Surg. Oncol., 2014, 23(3), 126-131.
[http://dx.doi.org/10.1016/j.suronc.2014.04.003] [PMID: 25031224]
[48]
Cho, W.C. Role of miRNAs in lung cancer. Expert Rev. Mol. Diagn., 2009, 9(8), 773-776.
[http://dx.doi.org/10.1586/erm.09.57] [PMID: 19895222]
[49]
Landi, M.T.; Chatterjee, N.; Yu, K.; Goldin, L.R.; Goldstein, A.M.; Rotunno, M.; Mirabello, L.; Jacobs, K.; Wheeler, W.; Yeager, M.; Bergen, A.W.; Li, Q.; Consonni, D.; Pesatori, A.C.; Wacholder, S.; Thun, M.; Diver, R.; Oken, M.; Virtamo, J.; Albanes, D.; Wang, Z.; Burdette, L.; Doheny, K.F.; Pugh, E.W.; Laurie, C.; Brennan, P.; Hung, R.; Gaborieau, V.; McKay, J.D.; Lathrop, M.; McLaughlin, J.; Wang, Y.; Tsao, M.S.; Spitz, M.R.; Wang, Y.; Krokan, H.; Vatten, L.; Skorpen, F.; Arnesen, E.; Benhamou, S.; Bouchard, C.; Metspalu, A.; Vooder, T.; Nelis, M.; Välk, K.; Field, J.K.; Chen, C.; Goodman, G.; Sulem, P.; Thorleifsson, G.; Rafnar, T.; Eisen, T.; Sauter, W.; Rosenberger, A.; Bickeböller, H.; Risch, A.; Chang-Claude, J.; Wichmann, H.E.; Stefansson, K.; Houlston, R.; Amos, C.I.; Fraumeni, J.F.; Savage, S.A.; Bertazzi, P.A.; Tucker, M.A.; Chanock, S.; Caporaso, N.E. A Genome-wide association study of lung cancer identifies a region of chromosome 5p15 associated with risk for adenocarcinoma. Am. J. Hum. Genet., 2011, 88(6), 861.
[http://dx.doi.org/10.1016/j.ajhg.2011.05.003] [PMID: 28472664]
[50]
Rodenhuis, S.; Slebos, R.J. Clinical significance of ras oncogene activation in human lung cancer. Cancer Res., 1992, 52(9)(Suppl.), 2665s-2669s.
[PMID: 1562997]
[51]
Marchetti, A.; Martella, C.; Felicioni, L.; Barassi, F.; Salvatore, S.; Chella, A.; Camplese, P.P.; Iarussi, T.; Mucilli, F.; Mezzetti, A.; Cuccurullo, F.; Sacco, R.; Buttitta, F. EGFR mutations in non-small-cell lung cancer: analysis of a large series of cases and development of a rapid and sensitive method for diagnostic screening with potential implications on pharmacologic treatment. J. Clin. Oncol., 2005, 23(4), 857-865.
[http://dx.doi.org/10.1200/JCO.2005.08.043] [PMID: 15681531]
[52]
Shigematsu, H.; Lin, L.; Takahashi, T.; Nomura, M.; Suzuki, M.; Wistuba, I.I.; Fong, K.M.; Lee, H.; Toyooka, S.; Shimizu, N.; Fujisawa, T.; Feng, Z.; Roth, J.A.; Herz, J.; Minna, J.D.; Gazdar, A.F. Clinical and biological features associated with epidermal growth factor receptor gene mutations in lung cancers. J. Natl. Cancer Inst., 2005, 97(5), 339-346.
[http://dx.doi.org/10.1093/jnci/dji055] [PMID: 15741570]
[53]
Iorio, M.V.; Ferracin, M.; Liu, C.G.; Veronese, A.; Spizzo, R.; Sabbioni, S.; Magri, E.; Pedriali, M.; Fabbri, M.; Campiglio, M.; Ménard, S.; Palazzo, J.P.; Rosenberg, A.; Musiani, P.; Volinia, S.; Nenci, I.; Calin, G.A.; Querzoli, P.; Negrini, M.; Croce, C.M. MicroRNA gene expression deregulation in human breast cancer. Cancer Res., 2005, 65(16), 7065-7070.
[http://dx.doi.org/10.1158/0008-5472.CAN-05-1783] [PMID: 16103053]
[54]
Raponi, M.; Dossey, L.; Jatkoe, T.; Wu, X.; Chen, G.; Fan, H.; Beer, D.G. MicroRNA classifiers for predicting prognosis of squamous cell lung cancer. Cancer Res., 2009, 69(14), 5776-5783.
[http://dx.doi.org/10.1158/0008-5472.CAN-09-0587] [PMID: 19584273]
[55]
Lu, J.; Getz, G.; Miska, E.A.; Alvarez-Saavedra, E.; Lamb, J.; Peck, D.; Sweet-Cordero, A.; Ebert, B.L.; Mak, R.H.; Ferrando, A.A.; Downing, J.R.; Jacks, T.; Horvitz, H.R.; Golub, T.R. MicroRNA expression profiles classify human cancers. Nature, 2005, 435(7043), 834-838.
[http://dx.doi.org/10.1038/nature03702] [PMID: 15944708]
[56]
Eisemann, N.; Waldmann, A.; Katalinic, A. Epidemiology of breast cancer - current figures and trends. Geburtshilfe Frauenheilkd., 2013, 73(2), 130-135.
[http://dx.doi.org/10.1055/s-0032-1328075] [PMID: 24771909]
[57]
Jemal, A.; Bray, F.; Center, M.M.; Ferlay, J.; Ward, E.; Forman, D. Global cancer statistics. CA Cancer J. Clin., 2011, 61(2), 69-90.
[http://dx.doi.org/10.3322/caac.20107] [PMID: 21296855]
[58]
Tang, J.; Ahmad, A.; Sarkar, F.H. MicroRNAs in breast cancer therapy. Curr. Pharm. Des., 2014, 20(33), 5268-5274.
[http://dx.doi.org/10.2174/1381612820666140128205239] [PMID: 24479805]
[59]
Rask, L.; Balslev, E.; Søkilde, R.; Høgdall, E.; Flyger, H.; Eriksen, J.; Litman, T. Differential expression of miR-139, miR-486 and miR-21 in breast cancer patients sub-classified according to lymph node status. Cell Oncol. (Dordr.), 2014, 37(3), 215-227.
[http://dx.doi.org/10.1007/s13402-014-0176-6] [PMID: 25027758]
[60]
Shen, S.; Sun, Q.; Liang, Z.; Cui, X.; Ren, X.; Chen, H.; Zhang, X.; Zhou, Y. A prognostic model of triple-negative breast cancer based on miR-27b-3p and node status. PLoS One, 2014, 9(6)e100664
[http://dx.doi.org/10.1371/journal.pone.0100664] [PMID: 24945253]
[61]
Xiong, D.D.; Lv, J.; Wei, K.L.; Feng, Z.B.; Chen, J.T.; Liu, K.C.; Chen, G.; Luo, D.Z. A nine-miRNA signature as a potential diagnostic marker for breast carcinoma: An integrated study of 1,110 cases. Oncol. Rep., 2017, 37(6), 3297-3304.
[http://dx.doi.org/10.3892/or.2017.5600] [PMID: 28440475]
[62]
van Laarhoven, T.; Nabuurs, S.B.; Marchiori, E. Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics, 2011, 27(21), 3036-3043.
[http://dx.doi.org/10.1093/bioinformatics/btr500] [PMID: 21893517]
[63]
Chen, X.; Yan, G.Y. Novel human lncRNA-disease association inference based on lncRNA expression profiles. Bioinformatics, 2013, 29(20), 2617-2624.
[http://dx.doi.org/10.1093/bioinformatics/btt426] [PMID: 24002109]

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