{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T16:47:03Z","timestamp":1772383623885,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,9,12]],"date-time":"2019-09-12T00:00:00Z","timestamp":1568246400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,9,12]],"date-time":"2019-09-12T00:00:00Z","timestamp":1568246400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61772381, 61572368"],"award-info":[{"award-number":["61772381, 61572368"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["2042017kf0219, 2042018kf0249"],"award-info":[{"award-number":["2042017kf0219, 2042018kf0249"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"crossref","award":["2018YFC0407904"],"award-info":[{"award-number":["2018YFC0407904"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Huazhong Agricultural University Scientific & Technological Self-innovation Foundation"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>MiRNAs play significant roles in many fundamental and important biological processes, and predicting potential miRNA-disease associations makes contributions to understanding the molecular mechanism of human diseases. Existing state-of-the-art methods make use of miRNA-target associations, miRNA-family associations, miRNA functional similarity, disease semantic similarity and known miRNA-disease associations, but the known miRNA-disease associations are not well exploited.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this paper, a network embedding-based multiple information integration method (NEMII) is proposed for the miRNA-disease association prediction. First, known miRNA-disease associations are formulated as a bipartite network, and the network embedding method Structural Deep Network Embedding (SDNE) is adopted to learn embeddings of nodes in the bipartite network. Second, the embedding representations of miRNAs and diseases are combined with biological features about miRNAs and diseases (miRNA-family associations and disease semantic similarities) to represent miRNA-disease pairs. Third, the prediction models are constructed based on the miRNA-disease pairs by using the random forest. In computational experiments, NEMII achieves high-accuracy performances and outperforms other state-of-the-art methods: GRNMF, NTSMDA and PBMDA. The usefulness of NEMII is further validated by case studies. The studies demonstrate the great potential of network embedding method for the miRNA-disease association prediction, and SDNE outperforms other popular network embedding methods: DeepWalk, High-Order Proximity preserved Embedding (HOPE) and Laplacian Eigenmaps (LE).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We propose a new method, named NEMII, for predicting miRNA-disease associations, which has great potential to benefit the field of miRNA-disease association prediction.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-019-3063-3","type":"journal-article","created":{"date-parts":[[2019,9,12]],"date-time":"2019-09-12T00:02:17Z","timestamp":1568246537000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["A network embedding-based multiple information integration method for the MiRNA-disease association prediction"],"prefix":"10.1186","volume":"20","author":[{"given":"Yuchong","family":"Gong","sequence":"first","affiliation":[]},{"given":"Yanqing","family":"Niu","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaohong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,12]]},"reference":[{"issue":"1","key":"3063_CR1","doi-asserted-by":"publisher","first-page":"45","DOI":"10.2174\/2211536603666140522003539","volume":"3","author":"Amanda Ribeiro","year":"2014","unstructured":"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\u201353.","journal-title":"MicroRNA"},{"issue":"5858","key":"3063_CR2","doi-asserted-by":"publisher","first-page":"1931","DOI":"10.1126\/science.1149460","volume":"318","author":"S Vasudevan","year":"2007","unstructured":"Vasudevan S, Tong Y, Steitz JA. Switching from repression to activation: MicroRNAs can up-regulate translation. Science. 2007;318(5858):1931\u20134.","journal-title":"Science"},{"issue":"5752","key":"3063_CR3","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.1126\/science.1121566","volume":"310","author":"K Xantha","year":"2005","unstructured":"Xantha K, Victor A. Developmental biology. Encountering microRNAs in cell fate signaling. Science. 2005;310(5752):1288.","journal-title":"Science"},{"issue":"5","key":"3063_CR4","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/j.gde.2005.08.005","volume":"15","author":"EA Miska","year":"2005","unstructured":"Miska EA. How microRNAs control cell division, differentiation and death. Curr Opin Genet Dev. 2005;15(5):563\u20138.","journal-title":"Curr Opin Genet Dev"},{"issue":"4","key":"3063_CR5","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1093\/nar\/gki200","volume":"33","author":"AM Cheng","year":"2005","unstructured":"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\u20137.","journal-title":"Nucleic Acids Res"},{"issue":"12","key":"3063_CR6","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1016\/j.tig.2004.09.010","volume":"20","author":"P Xu","year":"2004","unstructured":"Xu P, Guo M, Hay BA. MicroRNAs and the regulation of cell death. Trends Genet. 2004;20(12):617\u201324.","journal-title":"Trends Genet"},{"issue":"10","key":"3063_CR7","doi-asserted-by":"publisher","first-page":"e3420","DOI":"10.1371\/journal.pone.0003420","volume":"3","author":"L Ming","year":"2008","unstructured":"Ming L, Qipeng Z, Min D, Jing M, Yanhong G, Wei G, Qinghua C. An analysis of human microRNA and disease associations. PLoS One. 2008;3(10):e3420.","journal-title":"PLoS One"},{"issue":"16","key":"3063_CR8","doi-asserted-by":"publisher","first-page":"7065","DOI":"10.1158\/0008-5472.CAN-05-1783","volume":"65","author":"MV Iorio","year":"2005","unstructured":"Iorio MV, Manuela F, Chang-Gong L, Angelo V, Riccardo S, Silvia S, Eros M, Massimo P, Muller F, Manuela C. MicroRNA gene expression deregulation in human breast cancer. Cancer Res. 2005;65(16):7065.","journal-title":"Cancer Res"},{"issue":"12","key":"3063_CR9","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1161\/CIRCRESAHA.107.163147","volume":"101","author":"MV Latronico","year":"2007","unstructured":"Latronico MV, Catalucci D, Condorelli G. Emerging role of microRNAs in cardiovascular biology. Circ Res. 2007;101(12):1225\u201336.","journal-title":"Circ Res"},{"issue":"1","key":"3063_CR10","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1111\/j.1469-185X.2008.00061.x","volume":"84","author":"N Lynam-Lennon","year":"2010","unstructured":"Lynam-Lennon N, Maher SG, Reynolds JV. The roles of microRNA in cancer and apoptosis. Biol Rev Camb Philos Soc. 2010;84(1):55\u201371.","journal-title":"Biol Rev Camb Philos Soc"},{"issue":"7","key":"3063_CR11","doi-asserted-by":"publisher","first-page":"2224","DOI":"10.1039\/C6MB00049E","volume":"12","author":"D Sun","year":"2016","unstructured":"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.","journal-title":"Mol BioSyst"},{"issue":"3","key":"3063_CR12","doi-asserted-by":"publisher","first-page":"e1005455","DOI":"10.1371\/journal.pcbi.1005455","volume":"13","author":"ZH You","year":"2017","unstructured":"You ZH, Huang ZA, Zhu Z, Yan GY, Li ZW, 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.","journal-title":"PLoS Comput Biol"},{"issue":"7","key":"3063_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/15476286.2017.1312226","volume":"14","author":"X Chen","year":"2017","unstructured":"Chen X, Wu QF, Yan GY. RKNNMDA: ranking-based KNN for MiRNA-disease association prediction. RNA Biol. 2017;14(7):1.","journal-title":"RNA Biol"},{"issue":"2","key":"3063_CR14","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1093\/bioinformatics\/btx545","volume":"34","author":"Q Xiao","year":"2017","unstructured":"Xiao Q, Luo J, Liang C, Cai J, Ding P. A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations. Bioinformatics. 2017;34(2):239\u201348.","journal-title":"Bioinformatics"},{"issue":"24","key":"3063_CR15","doi-asserted-by":"crossref","first-page":"4256","DOI":"10.1093\/bioinformatics\/bty503","volume":"34","author":"X Chen","year":"2018","unstructured":"Chen X, Wang L, Qu J, Guan NN, Li JQ. Predicting miRNA-disease association based on inductive matrix completion. Bioinformatics. 2018;34(24):4256\u201365.","journal-title":"Bioinformatics"},{"key":"3063_CR16","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.neucom.2018.03.003","volume":"294","author":"Jiawei Luo","year":"2018","unstructured":"Luo J, Ding P, Liang C, Chen X. Semi-supervised prediction of human miRNA-disease association based on graph regularization framework in heterogeneous networks. Neurocomputing. 2018;294:29\u201338.","journal-title":"Neurocomputing"},{"issue":"18","key":"3063_CR17","doi-asserted-by":"publisher","first-page":"3178","DOI":"10.1093\/bioinformatics\/bty333","volume":"34","author":"Xing Chen","year":"2018","unstructured":"Chen X, Xie D, Wang L, Zhao Q, You ZH, Liu H. BNPMDA: bipartite network projection for MiRNA-disease association prediction. Bioinformatics. 2018,34(18):3178\u20133186.","journal-title":"Bioinformatics"},{"key":"3063_CR18","first-page":"701","volume-title":"DeepWalk: Online Learning of Social Representations","author":"B Perozzi","year":"2014","unstructured":"Perozzi B, Al-Rfou R, Skiena S. DeepWalk: Online Learning of Social Representations; 2014. p. 701\u201310."},{"key":"3063_CR19","doi-asserted-by":"crossref","unstructured":"Aditya Grover JL: node2vec: Scalable Feature Learning for Networks. In: Acm Sigkdd International Conference on Knowledge Discovery & Data Mining; 2016. p. 855\u2013864.","DOI":"10.1145\/2939672.2939754"},{"issue":"15","key":"3063_CR20","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1093\/bioinformatics\/btx160","volume":"33","author":"Nansu Zong","year":"2017","unstructured":"Zong N, Kim H, Ngo V, Harismendy O. Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations. Bioinformatics. 2017;33(15):2337\u20132344.","journal-title":"Bioinformatics"},{"key":"3063_CR21","doi-asserted-by":"publisher","first-page":"24032","DOI":"10.1109\/ACCESS.2017.2766758","volume":"5","author":"Guanghui Li","year":"2017","unstructured":"Li G, Luo J, Xiao Q, Liang C, Ding P, Cao B. Predicting MicroRNA-disease associations using network topological similarity based on DeepWalk. IEEE Access. 2017;5:24032\u201324039.","journal-title":"IEEE Access"},{"issue":"1","key":"3063_CR22","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1186\/s12859-018-2364-2","volume":"19","author":"X Liu","year":"2018","unstructured":"Liu X, Yang Z, Sang S, Zhou Z, Wang L, Zhang Y, Lin H, Wang J, Xu B. Identifying protein complexes based on node embeddings obtained from protein-protein interaction networks. Bmc Bioinformatics. 2018;19(1):332.","journal-title":"Bmc Bioinformatics"},{"issue":"6","key":"3063_CR23","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1162\/089976603321780317","volume":"15","author":"Mikhail Belkin","year":"2003","unstructured":"Belkin M, Niyogi P. Laplacian Eigenmaps for dimensionality reduction and data representation.\u00a0Neural Computation. 2003;15(6):1373\u20131396.","journal-title":"Neural Computation"},{"key":"3063_CR24","first-page":"1105","volume-title":"The ACM SIGKDD International Conference","author":"M Ou","year":"2016","unstructured":"Ou M, Cui P, Pei J, Zhang Z, Zhu W. Asymmetric transitivity preserving graph embedding. In: The ACM SIGKDD International Conference; 2016. p. 1105\u201314."},{"key":"3063_CR25","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1145\/2939672.2939753","volume-title":"ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"D Wang","year":"2016","unstructured":"Wang D, Cui P, Zhu W. Structural deep network embedding. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016. p. 1225\u201334."},{"issue":"4","key":"3063_CR26","doi-asserted-by":"publisher","first-page":"4915","DOI":"10.18632\/oncotarget.6642","volume":"7","author":"M Fassan","year":"2016","unstructured":"Fassan M, Saraggi D, Balsamo L, Cascione L, Castoro C, Coati I, Bernard MD, Farinati F, Guzzardo V, Valeri N. Let-7c down-regulation in helicobacter pylori -related gastric carcinogenesis. Oncotarget. 2016;7(4):4915\u201324.","journal-title":"Oncotarget"},{"issue":"9","key":"3063_CR27","doi-asserted-by":"publisher","first-page":"9951","DOI":"10.18632\/oncotarget.7127","volume":"7","author":"D Aslan","year":"2016","unstructured":"Aslan D, Garde C, Nygaard MK, Helbo AS, Dimopoulos K, Hansen JW, Severinsen MT, Treppendahl MB, Sj\u00f8 LD, Gr\u00f8nb\u00e6k K. Tumor suppressor microRNAs are downregulated in myelodysplastic syndrome with spliceosome mutations. Oncotarget. 2016;7(9):9951\u201363.","journal-title":"Oncotarget"},{"issue":"1","key":"3063_CR28","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1186\/1752-0509-7-104","volume":"7","author":"B Kazuhiko","year":"2013","unstructured":"Kazuhiko B, Kumar S, Rachel S, Pranavkumar S, Reena M, Stephanie W, Vivek K, Eric D, Jegga AG, Bezerra JA. Integrative genomics identifies candidate microRNAs for pathogenesis of experimental biliary atresia. BMC Syst Biol. 2013;7(1):104.","journal-title":"BMC Syst Biol"},{"issue":"23","key":"3063_CR29","first-page":"8374","volume":"22","author":"WJ Li","year":"2018","unstructured":"Li WJ, Xie XX, Bai J, Wang C, Zhao L, Jiang DQ. Increased expression of miR-1179 inhibits breast cancer cell metastasis by modulating notch signaling pathway and correlates with favorable prognosis. Eur Rev Med Pharmacol Sci. 2018;22(23):8374\u201382.","journal-title":"Eur Rev Med Pharmacol Sci"},{"issue":"3","key":"3063_CR30","doi-asserted-by":"publisher","first-page":"450","DOI":"10.7150\/jca.23151","volume":"9","author":"MJ Merino","year":"2018","unstructured":"Merino MJ, Gil S, Macias CG, Lara K. The unknown microRNA expression of male breast cancer. Similarities and differences with female ductal carcinoma. Their role as tumor biomarker. J Cancer. 2018;9(3):450\u20139.","journal-title":"J Cancer"},{"issue":"5","key":"3063_CR31","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1093\/bioinformatics\/btt014","volume":"29","author":"X Boya","year":"2013","unstructured":"Boya X, Qin D, Hongjin H, Di W. miRCancer: a microRNA-cancer association database constructed by text mining on literature. Bioinformatics. 2013;29(5):638\u201344.","journal-title":"Bioinformatics"},{"issue":"15","key":"3063_CR32","doi-asserted-by":"publisher","first-page":"19519","DOI":"10.18632\/oncotarget.6961","volume":"7","author":"B Phan","year":"2016","unstructured":"Phan B, Majid S, Ursu S, Semir DD, Nosrati M, Bezrookove V, Kashani-Sabet M, Dar AA. Tumor suppressor role of microRNA-1296 in triple-negative breast cancer. Oncotarget. 2016;7(15):19519\u201330.","journal-title":"Oncotarget"},{"issue":"3","key":"3063_CR33","doi-asserted-by":"publisher","first-page":"2155","DOI":"10.3892\/ol.2016.4217","volume":"11","author":"JY Hu","year":"2016","unstructured":"Hu JY, Yi W, Zhang MY, Xu R, Zeng LS, Long XR, Zhou XM, Zheng XS, Kang Y, Wang HY. MicroRNA-711 is a prognostic factor for poor overall survival and has an oncogenic role in breast cancer. Oncol Lett. 2016;11(3):2155\u201363.","journal-title":"Oncol Lett"},{"issue":"1","key":"3063_CR34","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.bbrc.2018.08.149","volume":"504","author":"Lingqin Song","year":"2018","unstructured":"Song L, Dai Z, Zhang S, Zhang H, Liu C, Ma X, Liu D, Zan Y, Yin X. MicroRNA-1179 suppresses cell growth and invasion by targeting sperm-associated antigen 5-mediated Akt signaling in human non-small cell lung cancer. Biochem Biophys Res Commun. 2018;504(1):164\u2013170.","journal-title":"Biochemical and Biophysical Research Communications"},{"issue":"4","key":"3063_CR35","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1016\/j.bbrc.2016.04.002","volume":"473","author":"W Jiang","year":"2016","unstructured":"Jiang W, Tian Y, Jiang S, Liu S, Zhao X, Tian D. MicroRNA-376c suppresses non-small-cell lung cancer cell growth and invasion by targeting LRH-1-mediated Wnt signaling pathway. Biochem Biophys Res Commun. 2016;473(4):980\u20136.","journal-title":"Biochem Biophys Res Commun"},{"issue":"6","key":"3063_CR36","doi-asserted-by":"publisher","first-page":"2509","DOI":"10.1159\/000495921","volume":"51","author":"S Hu","year":"2018","unstructured":"Hu S, Yuan Y, Song Z, Yan D, Kong X. Expression profiles of microRNAs in drug-resistant non-small cell lung Cancer cell lines using microRNA sequencing. Cell Physiol Biochem. 2018;51(6):2509\u201322.","journal-title":"Cell Physiol Biochem"},{"issue":"2","key":"3063_CR37","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1620\/tjem.232.85","volume":"232","author":"W Chaohui","year":"2014","unstructured":"Chaohui W, Yunpeng C, Zefeng H, Jianbing H, Chao H, Hongbing D, Jie J. Serum levels of miR-19b and miR-146a as prognostic biomarkers for non-small cell lung cancer. Tohoku J Exp Med. 2014;232(2):85\u201395.","journal-title":"Tohoku J Exp Med"},{"key":"3063_CR38","volume-title":"MicroRNA in development and in the progression of cancer","author":"RD Mohan","year":"2014","unstructured":"Mohan RD, Bibber B, Sinha G, Patel SA, Rameshwar P. MicroRNA in development and in the progression of cancer; 2014."},{"issue":"5","key":"3063_CR39","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1177\/1535370216681554","volume":"242","author":"AA Moustafa","year":"2017","unstructured":"Moustafa AA, Ziada M, Elshaikh A, Datta A, Kim H, Moroz K, Srivastav S, Thomas R, Silberstein JL, Moparty K, et al. Identification of microRNA signature and potential pathway targets in prostate cancer. Exp Biol Med. 2017;242(5):536\u201346.","journal-title":"Exp Biol Med"},{"issue":"2","key":"3063_CR40","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.medici.2016.02.007","volume":"52","author":"K Stuopelyte","year":"2016","unstructured":"Stuopelyte K, Daniunaite K, Jankevicius F, Jarmalaite S. Detection of miRNAs in urine of prostate cancer patients. Medicina. 2016;52(2):116\u201324.","journal-title":"Medicina"},{"key":"3063_CR41","volume-title":"The top 50 prostatic neoplasms-related miRNA candidates","author":"X Ping","year":"2013","unstructured":"Ping X, Ke H, Maozu G, Yahong G, Jinbao L, Jian D, Yong L, Qiguo D, Jin L, Zhixia T et al: The top 50 prostatic neoplasms-related miRNA candidates; 2013."},{"issue":"Database issue","key":"3063_CR42","first-page":"D1070","volume":"42","author":"L Yang","year":"2014","unstructured":"Yang L, Chengxiang Q, Jian T, Bin G, Jichun Y, Tianzi J, Qinghua C. HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res. 2014;42(Database issue):D1070.","journal-title":"Nucleic Acids Res"},{"issue":"Suppl 4","key":"3063_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2164-11-S3-I1","volume":"11","author":"Z Yang","year":"2010","unstructured":"Yang Z, Ren F, Liu C, He S, Sun G, Gao Q, Yao L, Zhang Y, Miao R, Cao Y. dbDEMC: a database of differentially expressed miRNAs in human cancers. BMC Genomics. 2010;11(Suppl 4):1\u20138.","journal-title":"BMC Genomics"},{"issue":"1","key":"3063_CR44","doi-asserted-by":"publisher","first-page":"D98","DOI":"10.1093\/nar\/gkn714","volume":"37","author":"Q Jiang","year":"2009","unstructured":"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. Nucleic Acids Res. 2009;37(1):D98\u2013104.","journal-title":"Nucleic Acids Res"},{"issue":"Database issue","key":"3063_CR45","doi-asserted-by":"publisher","first-page":"D1070","DOI":"10.1093\/nar\/gkt1023","volume":"42","author":"Y Li","year":"2014","unstructured":"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 Res. 2014;42(Database issue):D1070.","journal-title":"Nucleic Acids Res"},{"issue":"suppl_1","key":"3063_CR46","doi-asserted-by":"publisher","first-page":"D152","DOI":"10.1093\/nar\/gkq1027","volume":"39","author":"A Kozomara","year":"2011","unstructured":"Kozomara A, Griffiths-Jones S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011;39(suppl_1):D152\u20137.","journal-title":"Nucleic Acids Res"},{"key":"3063_CR47","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.neucom.2018.01.085","volume":"287","author":"Wen Zhang","year":"2018","unstructured":"Wen Z, Liu X, Chen Y, Wu W, Li X. Feature-derived graph regularized matrix factorization for predicting drug side effects. Neurocomputing. 2018;287:154\u2013162.","journal-title":"Neurocomputing"},{"key":"3063_CR48","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.ymeth.2018.06.001","volume":"145","author":"Wen Zhang","year":"2018","unstructured":"Wen Z, Xiang Y, Feng H, Ruoqi L, Yanlin C, Chunyang R. Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network. Methods. 2018;145:51\u201359.","journal-title":"Methods"},{"issue":"1","key":"3063_CR49","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1186\/s12859-018-2220-4","volume":"19","author":"Z Wen","year":"2018","unstructured":"Wen Z, Xiang Y, Weiran L, Wenjian W, Ruoqi L, Feng H, Feng L. Predicting drug-disease associations by using similarity constrained matrix factorization. Bmc Bioinformatics. 2018;19(1):233.","journal-title":"Bmc Bioinformatics"},{"key":"3063_CR50","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.jbi.2018.11.005","volume":"88","author":"Wen Zhang","year":"2018","unstructured":"Zhang W, Chen Y, Li D, Yue X. Manifold regularized matrix factorization for drug-drug interaction prediction. J Biomed Inform. 2018;88:90\u201397.","journal-title":"Journal of Biomedical Informatics"},{"key":"3063_CR51","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.ins.2019.05.017","volume":"497","author":"W Zhang","year":"2019","unstructured":"Zhang W, Jing K, Huang F, Chen Y, Li B, Li J, Gong J. SFLLN: a sparse feature learning ensemble method with linear neighborhood regularization for predicting drug\u2013drug interactions. Inf Sci. 2019;497:189\u2013201.","journal-title":"Inf Sci"},{"key":"3063_CR52","doi-asserted-by":"publisher","first-page":"83474","DOI":"10.1109\/ACCESS.2019.2920942","volume":"7","author":"W Zhang","year":"2019","unstructured":"Zhang W, Yu C, Wang X, Liu F. Predicting CircRNA-disease associations through linear neighborhood label propagation method. IEEE Access. 2019;7:83474\u201383.","journal-title":"IEEE Access"},{"issue":"12","key":"3063_CR53","doi-asserted-by":"publisher","first-page":"e1006616","DOI":"10.1371\/journal.pcbi.1006616","volume":"14","author":"W Zhang","year":"2018","unstructured":"Zhang W, Yue X, Tang G, Wu W, Huang F, Zhang X, Ioshikhes I. SFPEL-LPI: sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions. PLoS Comput Biol. 2018;14(12):e1006616.","journal-title":"PLoS Comput Biol"},{"key":"3063_CR54","first-page":"1225","volume-title":"Structural Deep Network Embedding","author":"D Wang","year":"2016","unstructured":"Wang D, Cui P, Zhu W. Structural Deep Network Embedding; 2016. p. 1225\u201334."},{"key":"3063_CR55","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.knosys.2018.03.022","volume":"151","author":"P Goyal","year":"2018","unstructured":"Goyal P, Ferrara E. Graph embedding techniques, applications, and performance: a survey. Knowl-Based Syst. 2018;151:78\u201394.","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"3063_CR56","first-page":"17","volume":"53","author":"Elsevier","year":"2012","unstructured":"Elsevier. International Journal of Approximate Reasoning. Mathware Soft Comput. 2012;53(1):17\u201329.","journal-title":"Mathware Soft Comput"},{"issue":"1","key":"3063_CR57","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45(1):5\u201332.","journal-title":"Mach Learn"},{"key":"3063_CR58","volume-title":"Using random forest to learn imbalanced data","author":"C Chen","year":"2004","unstructured":"Chen C, Breiman L. Using random forest to learn imbalanced data; 2004."},{"issue":"3","key":"3063_CR59","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1093\/bioinformatics\/btx614","volume":"34","author":"G Taherzadeh","year":"2018","unstructured":"Taherzadeh G, Zhou Y, Liew AW, Yang Y. Structure-based prediction of protein- peptide binding regions using random Forest. Bioinformatics. 2018;34(3):477\u201384.","journal-title":"Bioinformatics"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3063-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-019-3063-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3063-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T04:37:02Z","timestamp":1695184622000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-019-3063-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,12]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["3063"],"URL":"https:\/\/doi.org\/10.1186\/s12859-019-3063-3","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,12]]},"assertion":[{"value":"17 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"468"}}