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The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http:\/\/lab.malab.cn\/\u223cacy\/NmRF.<\/jats:p>","DOI":"10.1093\/bib\/bbab480","type":"journal-article","created":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T20:57:33Z","timestamp":1634936253000},"source":"Crossref","is-referenced-by-count":50,"title":["NmRF: identification of multispecies RNA 2\u2019-O-methylation modification sites from RNA sequences"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3008-6357","authenticated-orcid":false,"given":"Chunyan","family":"Ao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an, China"}]},{"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China"},{"name":"Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8351-3332","authenticated-orcid":false,"given":"Liang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an, China"}]}],"member":"286","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"key":"2022012000302551600_ref1","doi-asserted-by":"crossref","first-page":"D327","DOI":"10.1093\/nar\/gkx934","article-title":"RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data","volume":"46","author":"Xuan","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2022012000302551600_ref2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1093\/bib\/bbv033","article-title":"Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks","volume":"17","author":"Zeng","year":"2016","journal-title":"Brief 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