{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:52:10Z","timestamp":1760151130612,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Project of Hunan Provincial Department of Education","award":["20C0480 and 17C0133"],"award-info":[{"award-number":["20C0480 and 17C0133"]}]},{"name":"Project of Science and Technology Plan of Changsha","award":["ZD1601071"],"award-info":[{"award-number":["ZD1601071"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>miRNAs are a category of important endogenous non-coding small RNAs and are ubiquitous in eukaryotes. They are widely involved in the regulatory process of post-transcriptional gene expression and play a critical part in the development of human diseases. By utilizing recent advancements in big data technology, using bioinformatics methods to identify causative miRNA becomes a hot spot. In this paper, a method called RNSSLFN is proposed to identify the miRNA-disease associations by reliable negative sample selection and an improved single-hidden layer feedforward neural network (SLFN). It involves, firstly, obtaining integrated similarity for miRNAs and diseases; next, selecting reliable negative samples from unknown miRNA-disease associations via distinguishing up-regulated or down-regulated miRNAs; then, introducing an improved SLFN to solve the prediction task. The experimental results on the latest data sets HMDD v3.2 and the framework of 5-fold cross-validation (CV) show that the average AUC and AUPR of RNSSLFN achieve 0.9316 and 0.9065 m, respectively, which are superior to the other three state-of-the-art methods. Furthermore, in the case studies of 10 common cancers, more than 70% of the top 30 predicted miRNA-disease association pairs are verified in the databases, which further confirms the reliability and effectiveness of the RNSSLFN model. Generally, RNSSLFN in predicting miRNA-disease associations has prodigious potential and extensive foreground.<\/jats:p>","DOI":"10.3390\/info13030108","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A miRNA-Disease Association Identification Method Based on Reliable Negative Sample Selection and Improved Single-Hidden Layer Feedforward Neural Network"],"prefix":"10.3390","volume":"13","author":[{"given":"Qinglong","family":"Tian","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Applied Mathematics, Changsha University, Changsha 410022, China"}]},{"given":"Su","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"}]},{"given":"Qi","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Life Science, Anhui Agriculture University, Hefei 230036, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1146\/annurev-biochem-060308-103103","article-title":"Regulation of mRNA translation and stability by microRNAs","volume":"79","author":"Fabian","year":"2010","journal-title":"Annu. Rev. Biochem."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.cell.2009.01.002","article-title":"MicroRNAs: Target Recognition and Regulatory Functions","volume":"136","author":"Bartel","year":"2009","journal-title":"Cell"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1016\/0092-8674(93)90529-Y","article-title":"The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14","volume":"75","author":"Lee","year":"1993","journal-title":"Cell"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.molcel.2010.03.013","article-title":"Posttranscriptional Regulation of MicroRNA Biogenesis in Animals","volume":"38","author":"Siomi","year":"2010","journal-title":"Mol. Cell"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1152\/physrev.00006.2010","article-title":"MicroRNAs in Development and Disease","volume":"91","author":"Sayed","year":"2011","journal-title":"Physiol. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1146\/annurev.genom.8.080706.092351","article-title":"microRNAs in Vertebrate Physiology and Human Disease","volume":"8","author":"Chang","year":"2007","journal-title":"Annu. Rev. Genom. Hum. Genet."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.3389\/fgene.2019.01234","article-title":"Identifying Potential miRNAs\u2013Disease Associations With Probability Matrix Factorization","volume":"10","author":"Xu","year":"2019","journal-title":"Front. Genet."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, X., Yin, J., Qu, J., Huang, L., and Wang, E. (2018). MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction. PLoS Comput. Biol., 8.","DOI":"10.1371\/journal.pcbi.1006418"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ha, J., Park, C., Park, C., and Park, S. (2020). Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization. Cells, 3.","DOI":"10.3390\/cells9040881"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"78847","DOI":"10.1109\/ACCESS.2021.3084148","article-title":"MLMD: Metric Learning for predicting miRNA-Disease associations","volume":"5","author":"Ha","year":"2021","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"21187","DOI":"10.18632\/oncotarget.15061","article-title":"MCMDA: Matrix completion for miRNA-disease association prediction","volume":"28","author":"Li","year":"2017","journal-title":"Oncotarget"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103358","DOI":"10.1016\/j.jbi.2019.103358","article-title":"IMIPMF: Inferring miRNA-disease interactions using probabilistic matrix factorization","volume":"102","author":"Ha","year":"2020","journal-title":"J Biomed Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"123146","DOI":"10.1109\/ACCESS.2021.3109806","article-title":"Big-ECG: Cardiographic Predictive Cyber-Physical System for Stroke Management","volume":"9","author":"Hussain","year":"2021","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"213574","DOI":"10.1109\/ACCESS.2020.3040437","article-title":"HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics","volume":"8","author":"Hussain","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Perez-Iratxeta, C., Wjst, M., Bork, P., and Andrade, M.A. (2005). G2D: A tool for mining genes associated with disease. BMC Genet., 6.","DOI":"10.1186\/1471-2156-6-45"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6658","DOI":"10.1038\/s41598-020-63735-9","article-title":"Predicting miRNA-disease association from heterogeneous information network with GraRep embedding model","volume":"10","author":"Ji","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"80728","DOI":"10.1109\/ACCESS.2020.2990533","article-title":"LRMCMDA: Predicting miRNA-Disease Association by Integrating Low-Rank Matrix Completion With miRNA and Disease Similarity Information","volume":"8","author":"Xu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3178","DOI":"10.1093\/bioinformatics\/bty333","article-title":"BNPMDA: Bipartite Network Projection for MiRNA\u2013Disease Association prediction","volume":"34","author":"Chen","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/s11042-017-5291-8","article-title":"CFMDA: Collaborative filtering-based miRNA-disease association prediction","volume":"78","author":"Li","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1007\/s00438-018-1438-1","article-title":"HNMDA: Heterogeneous network-based miRNA\u2013disease association prediction","volume":"293","author":"Peng","year":"2018","journal-title":"Mol. Genet. Genom."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TCBB.2018.2883041","article-title":"NTSHMDA: Prediction of Human Microbe-Disease Association Based on Random Walk by Integrating Network Topological Similarity","volume":"17","author":"Luo","year":"2018","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinforma."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1002\/cpt.1796","article-title":"An Introduction to Machine Learning","volume":"107","author":"Badillo","year":"2020","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yin, J., and Zhang, X. (2018). A Semi-Supervised Learning Algorithm for Predicting Four Types miRNA-Disease Associations by Mutual Information in a Heterogeneous Network. Genes, 9.","DOI":"10.3390\/genes9030139"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.neucom.2020.09.032","article-title":"A neural collaborative filtering method for identifying miRNA-disease associations","volume":"422","author":"Liu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"(2019). Xuan; Zhang; Zhang; Li; Zhao Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information. Genes, 10.","DOI":"10.3390\/genes10090685"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Che, K., Guo, M., Wang, C., Liu, X., and Chen, X. (2019). Predicting miRNA-Disease Association by Latent Feature Extraction with Positive Samples. Genes, 10.","DOI":"10.3390\/genes10020080"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4730","DOI":"10.1093\/bioinformatics\/btz297","article-title":"Adaptive boosting-based computational model for predicting potential miRNA-disease associations","volume":"35","author":"Zhao","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"133170","DOI":"10.1109\/ACCESS.2020.3006998","article-title":"Degree-Based Similarity Indexes for Identifying Potential miRNA-Disease Associations","volume":"8","author":"Meng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1080\/15476286.2017.1312226","article-title":"RKNNMDA: Ranking-based KNN for miRNA-Disease Association prediction","volume":"14","author":"Chen","year":"2017","journal-title":"RNA Biol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1093\/bioinformatics\/btv039","article-title":"Prediction of potential disease-associated microRNAs based on random walk","volume":"31","author":"Xuan","year":"2015","journal-title":"Bioinformatics"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5501","DOI":"10.1038\/srep05501","article-title":"Semi-supervised learning for potential human microRNA-disease associations inference","volume":"4","author":"Chen","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, L., Liu, B., Li, Z., Zhu, X., Liang, Z., and An, J. (2020). Predicting miRNA-disease associations by multiple meta-paths fusion graph embedding model. BMC Bioinform., 21.","DOI":"10.1186\/s12859-020-03765-2"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"D1070","DOI":"10.1093\/nar\/gkt1023","article-title":"HMDD v2.0: A database for experimentally supported human microRNA and disease associations","volume":"42","author":"Li","year":"2014","journal-title":"Nucleic Acids Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1093\/bioinformatics\/btt014","article-title":"miRCancer: A microRNA-cancer association database constructed by text mining on literature","volume":"29","author":"Xie","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"D812","DOI":"10.1093\/nar\/gkw1079","article-title":"dbDEMC 2.0: Updated database of differentially expressed miRNAs in human cancers","volume":"45","author":"Yang","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"D98","DOI":"10.1093\/nar\/gkn714","article-title":"miR2Disease: A manually curated database for microRNA deregulation in human disease","volume":"37","author":"Jiang","year":"2009","journal-title":"Nucleic Acids Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lu, M., Zhang, Q., Deng, M., Miao, J., Guo, Y., Gao, W., and Cui, Q. (2008). An Analysis of Human MicroRNA and Disease Associations. PLoS ONE, 3.","DOI":"10.1371\/journal.pone.0003420"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"D1013","DOI":"10.1093\/nar\/gky1010","article-title":"HMDD v3.0: A database for experimentally supported human microRNA\u2013disease associations","volume":"47","author":"Huang","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"bbaa140","DOI":"10.1093\/bib\/bbaa140","article-title":"Tensor decomposition with relational constraints for predicting multiple types of microRNA-disease associations","volume":"22","author":"Huang","year":"2021","journal-title":"Brief. Bioinform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1093\/bioinformatics\/btq241","article-title":"Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases","volume":"26","author":"Wang","year":"2010","journal-title":"Bioinformatics"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"107200","DOI":"10.1016\/j.compbiolchem.2020.107200","article-title":"Predicting potential miRNA-disease associations by combining gradient boosting decision tree with logistic regression","volume":"85","author":"Zhou","year":"2020","journal-title":"Comput. Biol. Chem."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xuan, P., Han, K., Guo, M., Guo, Y., Li, J., Ding, J., Liu, Y., Dai, Q., Li, J., and Teng, Z. (2013). Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors. PLoS ONE, 8.","DOI":"10.1371\/annotation\/28592478-72f5-4937-919b-b2342d6ceda0"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3036","DOI":"10.1093\/bioinformatics\/btr500","article-title":"Gaussian interaction profile kernels for predicting drug-target interaction","volume":"27","author":"Nabuurs","year":"2011","journal-title":"Bioinformatics"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"W536","DOI":"10.1093\/nar\/gkz328","article-title":"MISIM v2.0: A web server for inferring microRNA functional similarity based on microRNA-disease associations","volume":"47","author":"Li","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cheng, Q., Zhang, M., Li, Z., Cao, Y., He, B., and Feng, W. (2020, January 27\u201329). A Classfication Algorithm based on Self-organizing Neural Network Using Growing-Combination Structure. Proceedings of the 2020 39th Chinese Control Conference (CCC), Shenyang, China.","DOI":"10.23919\/CCC50068.2020.9188927"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s10585-015-9724-3","article-title":"MiR-9 and miR-21 as prognostic biomarkers for recurrence in papillary thyroid cancer","volume":"32","author":"Sondermann","year":"2015","journal-title":"Clin. Exp. Metastasis"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2825","DOI":"10.18632\/oncotarget.13747","article-title":"MicroRNA-497 inhibits thyroid cancer tumor growth and invasion by suppressing BDNF","volume":"8","author":"Wang","year":"2017","journal-title":"Oncotarget"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3526","DOI":"10.1038\/onc.2009.211","article-title":"Loss of miR-122 expression in liver cancer correlates with suppression of the hepatic phenotype and gain of metastatic properties","volume":"28","author":"Coulouarn","year":"2009","journal-title":"Oncogene"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1002\/hep.23904","article-title":"MicroRNA-125b suppressesed human liver cancer cell proliferation and metastasis by directly targeting oncogene LIN28B2","volume":"52","author":"Liang","year":"2010","journal-title":"Hepatology"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/3\/108\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:26:52Z","timestamp":1760135212000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/3\/108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,24]]},"references-count":49,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["info13030108"],"URL":"https:\/\/doi.org\/10.3390\/info13030108","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2022,2,24]]}}}