Journal
SCIENTIFIC REPORTS
Volume 10, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-020-77824-2
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Funding
- Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) from the Ministry of Trade, Industry and Energy, Republic of Korea [20204010600470]
- Brain Research Program of the National Research Foundation (NRF) - Korean government (MSIT) [NRF-2017M3C7A1044816]
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N4-acetylcytidine (ac4C) is a post-transcriptional modification in mRNA which plays a major role in the stability and regulation of mRNA translation. The working mechanism of ac4C modification in mRNA is still unclear and traditional laboratory experiments are time-consuming and expensive. Therefore, we propose an XG-ac4C machine learning model based on the eXtreme Gradient Boost classifier for the identification of ac4C sites. The XG-ac4C model uses a combination of electron-ion interaction pseudopotentials and electron-ion interaction pseudopotentials of trinucleotide of the nucleotides in ac4C sites. Moreover, Shapley additive explanations and local interpretable model-agnostic explanations are applied to understand the importance of features and their contribution to the final prediction outcome. The obtained results demonstrate that XG-ac4C outperforms existing state-of-the-art methods. In more detail, the proposed model improves the area under the precision-recall curve by 9.4% and 9.6% in cross-validation and independent tests, respectively. Finally, a user-friendly web server based on the proposed model for ac4C site identification is made freely available at http://nsclbio.jbnu.ac.kr/tools/xgac4c/.
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