Journal article

An Interpretable Prediction Model for Identifying N-7-Methylguanosine Sites Based on XGBoost and SHAP

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

Molecular Therapy : Nucleic Acids | CELL PRESS | Published : 2020

Abstract

Recent 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 ..

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University of Melbourne Researchers

Grants

Awarded by Fundamental Research Funds for the Central Universities


Awarded by National Natural Science Foundation of Liaoning Province


Awarded by National Health and Medical Research Council of Australia (NHMRC)


Awarded by Australian Research Council (ARC)


Funding Acknowledgements

This work was supported by Fundamental Research Funds for the Central Universities (3132020170 and 3132019323) and the National Natural Science Foundation of Liaoning Province (20180550307). This work was also supported by the National Health and Medical Research Council of Australia (NHMRC) (1144652 and 1127948), the Australian Research Council (ARC) (LP110200333 and DP120104460), and by a Major Inter-Disciplinary Research (IDR) project awarded by Monash University.