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dc.contributor.authorBi, Y
dc.contributor.authorXiang, D
dc.contributor.authorGe, Z
dc.contributor.authorLi, F
dc.contributor.authorJia, C
dc.contributor.authorSong, J
dc.date.accessioned2020-12-09T22:27:51Z
dc.date.available2020-12-09T22:27:51Z
dc.date.issued2020-12-04
dc.identifierpii: S2162-2531(20)30251-1
dc.identifier.citationBi, Y., Xiang, D., Ge, Z., Li, F., Jia, C. & Song, J. (2020). An Interpretable Prediction Model for Identifying N-7-Methylguanosine Sites Based on XGBoost and SHAP. MOLECULAR THERAPY-NUCLEIC ACIDS, 22, pp.362-372. https://doi.org/10.1016/j.omtn.2020.08.022.
dc.identifier.issn2162-2531
dc.identifier.urihttp://hdl.handle.net/11343/252972
dc.description.abstractRecent studies have increasingly shown that the chemical modification of mRNA plays an important role in the regulation of gene expression. N7-methylguanosine (m7G) is a type of positively-charged mRNA modification that plays an essential role for efficient gene expression and cell viability. However, the research on m7G has received little attention to date. Bioinformatics tools can be applied as auxiliary methods to identify m7G sites in transcriptomes. In this study, we develop a novel interpretable machine learning-based approach termed XG-m7G for the differentiation of m7G sites using the XGBoost algorithm and six different types of sequence-encoding schemes. Both 10-fold and jackknife cross-validation tests indicate that XG-m7G outperforms iRNA-m7G. Moreover, using the powerful SHAP algorithm, this new framework also provides desirable interpretations of the model performance and highlights the most important features for identifying m7G sites. XG-m7G is anticipated to serve as a useful tool and guide for researchers in their future studies of mRNA modification sites.
dc.languageEnglish
dc.publisherCELL PRESS
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.titleAn Interpretable Prediction Model for Identifying N-7-Methylguanosine Sites Based on XGBoost and SHAP
dc.typeJournal Article
dc.identifier.doi10.1016/j.omtn.2020.08.022
melbourne.affiliation.departmentMicrobiology and Immunology
melbourne.source.titleMolecular Therapy : Nucleic Acids
melbourne.source.volume22
melbourne.source.pages362-372
dc.rights.licenseCC BY-NC-ND
melbourne.elementsid1478720
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC7533297
melbourne.contributor.authorLi, Fuyi
dc.identifier.eissn2162-2531
melbourne.accessrightsOpen Access


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