{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T18:42:57Z","timestamp":1775932977987,"version":"3.50.1"},"reference-count":64,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Future Scientists Program of China University of Mining and Technology","award":["2020WLKXJ027"],"award-info":[{"award-number":["2020WLKXJ027"]}]},{"name":"Postgraduate Research & Practice Innovation Program of Jiangsu Province","award":["KYCX20_1994"],"award-info":[{"award-number":["KYCX20_1994"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>MicroRNAs (miRNAs) play crucial roles in human disease and can be targeted by small molecule (SM) drugs according to numerous studies, which shows that identifying SM\u2013miRNA associations in human disease is important for drug development and disease treatment. We proposed the method of Ensemble of Kernel Ridge Regression-based Small Molecule\u2013MiRNA Association prediction (EKRRSMMA) to uncover potential SM\u2013miRNA associations by combing feature dimensionality reduction and ensemble learning. First, we constructed different feature subsets for both SMs and miRNAs. Then, we trained homogeneous base learners based on distinct feature subsets and took the average of scores obtained from these base learners as SM\u2013miRNA association score. In EKRRSMMA, feature dimensionality reduction technology was employed in the process of construction of feature subsets to reduce the influence of noisy data. Besides, the base learner, namely KRR_avg, was the combination of two classifiers constructed under SM space and miRNA space, which could make full use of the information of SM and miRNA. To assess the prediction performance of EKRRSMMA, we conducted Leave-One-Out Cross-Validation (LOOCV), SM-fixed local LOOCV, miRNA-fixed local LOOCV and 5-fold CV based on two datasets. For Dataset 1 (Dataset 2), EKRRSMMA got the Area Under receiver operating characteristic Curves (AUCs) of 0.9793 (0.8871), 0.8071 (0.7705), 0.9732 (0.8586) and 0.9767 \u00b1 0.0014 (0.8560 \u00b1 0.0027). Besides, we conducted four case studies. As a result, 32 (5-Fluorouracil), 19 (17\u03b2-Estradiol), 26 (5-Aza-2\u2032-deoxycytidine) and 11 (cyclophosphamide) out of top 50 predicted potentially associated miRNAs were confirmed by database or experimental literature. Above evaluation results demonstrated that EKRRSMMA is reliable for predicting SM\u2013miRNA associations.<\/jats:p>","DOI":"10.1093\/bib\/bbab431","type":"journal-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T11:10:51Z","timestamp":1632136251000},"source":"Crossref","is-referenced-by-count":48,"title":["Ensemble of kernel ridge regression-based small molecule\u2013miRNA association prediction in human disease"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6795-4007","authenticated-orcid":false,"given":"Chun-Chun","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi-Chi","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9028-5342","authenticated-orcid":false,"given":"Xing","family":"Chen","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"2022011921335523400_ref1","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1038\/nature02871","article-title":"The functions of animal microRNAs","volume":"431","author":"Ambros","year":"2004","journal-title":"Nature"},{"key":"2022011921335523400_ref2","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/S0092-8674(04)00045-5","article-title":"MicroRNAs: genomics, biogenesis, mechanism, and function","volume":"116","author":"Bartel","year":"2004","journal-title":"Cell"},{"key":"2022011921335523400_ref3","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1038\/nature02873","article-title":"Mechanisms of gene silencing by double-stranded RNA","volume":"431","author":"Meister","year":"2004","journal-title":"Nature"},{"key":"2022011921335523400_ref4","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1016\/S0092-8674(01)00616-X","article-title":"MicroRNAs: tiny regulators with great potential","volume":"107","author":"Ambros","year":"2001","journal-title":"Cell"},{"key":"2022011921335523400_ref5","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1093\/nar\/gki200","article-title":"Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis","volume":"33","author":"Cheng","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2022011921335523400_ref6","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1126\/science.1121566","article-title":"Developmental biology. 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