{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Dou L"],"funding":["Natural Science Foundation of China","Scientific Research Foundation","Natural Science Foundation of Guangdong Province","Post-doctoral Foundation Project of Shenzhen Polytechnic"],"pagination":["2236-2246"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8632091"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(12)"],"pubmed_abstract":["As one of the common post-transcriptional modifications in tRNAs, dihydrouridine (D) has prominent effects on regulating the flexibility of tRNA as well as cancerous diseases. Facing with the expensive and time-consuming sequencing techniques to detect D modification, precise computational tools can largely promote the progress of molecular mechanisms and medical developments. We proposed a novel predictor, called iRNAD_XGBoost, to identify potential D sites using multiple RNA sequence representations. In this method, by considering the imbalance problem using hybrid sampling method SMOTEEEN, the XGBoost-selected top 30 features are applied to construct model. The optimized model showed high <i>Sn</i> and <i>Sp</i> values of 97.13% and 97.38% over jackknife test, respectively. For the inde"],"journal":["RNA biology"],"pubmed_title":["Accurate identification of RNA D modification using multiple features."],"pmcid":["PMC8632091"],"funding_grant_id":["2018A0303130084","61902259","6020330003K","JCYJ20170818100431895"],"pubmed_authors":["Xu L","Dou L","Han K","Zhou W","Zhang L"],"additional_accession":[]},"is_claimable":false,"name":"Accurate identification of RNA D modification using multiple features.","description":"As one of the common post-transcriptional modifications in tRNAs, dihydrouridine (D) has prominent effects on regulating the flexibility of tRNA as well as cancerous diseases. Facing with the expensive and time-consuming sequencing techniques to detect D modification, precise computational tools can largely promote the progress of molecular mechanisms and medical developments. We proposed a novel predictor, called iRNAD_XGBoost, to identify potential D sites using multiple RNA sequence representations. In this method, by considering the imbalance problem using hybrid sampling method SMOTEEEN, the XGBoost-selected top 30 features are applied to construct model. The optimized model showed high <i>Sn</i> and <i>Sp</i> values of 97.13% and 97.38% over jackknife test, respectively. For the inde","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Dec","modification":"2025-06-01T02:32:37.077Z","creation":"2025-06-01T02:32:37.077Z"},"accession":"S-EPMC8632091","cross_references":{"pubmed":["33729104"],"doi":["10.1080/15476286.2021.1898160"]}}