An Interpretable Prediction Model for Identifying N<sup>7</sup>-Methylguanosine Sites Based on XGBoost and SHAP.

Bi, Yue; Xiang, Dongxu; Ge, Zongyuan; Li, Fuyi; Jia, Cangzhi; Song, Jiangning

An Interpretable Prediction Model for Identifying N⁷-Methylguanosine Sites Based on XGBoost and SHAP.

Bi, Yue; Xiang, Dongxu; Ge, Zongyuan; Li, Fuyi; Jia, Cangzhi; Song, Jiangning.

Afiliação

Bi Y; School of Science, Dalian Maritime University, Dalian 116026, China.
Xiang D; Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.
Ge Z; Monash e-Research Centre and Faculty of Engineering, Monash University, Melbourne, VIC 3800, Australia.
Li F; Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.
Jia C; School of Science, Dalian Maritime University, Dalian 116026, China.
Song J; Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.

Mol Ther Nucleic Acids ; 22: 362-372, 2020 Dec 04.

Article em En | MEDLINE | ID: mdl-33230441

Palavras-chave

ENAC; N7-Methylguanosine; SCPseDNC; SHAP; XGBoost; feature selection; m7G; machine learning; model interpretation; prediction

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Mol Ther Nucleic Acids Ano de publicação: 2020 Tipo de documento: Article País de afiliação: China

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