ArticleScientific reports2020
XG-ac4C: identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 62 citations in OpenAlex.
- The functions, mechanisms and clinical relevance of RNA and protein acetyltransferase NAT10: a comprehensive review.Cell biology and toxicology · 2026Review
- Identifying RNA acInterdisciplinary sciences, computational life sciences · 2026Article
- Integrative interpretable learning reveals shared patterns of epitranscriptomic regulation across multiple cancer types.BMC biology · 2026Article
- ac4C modification sites prediction in human mRNA: a complete review.Briefings in bioinformatics · 2026Review
- EvoRMD: integrating biological context and evolutionary RNA language models for interpretable prediction of RNA modifications.Genome biology · 2026Article
- Review
- RNA N4-acetylcytidine modification in human cancers: from molecular function to oncogenic mechanisms.iScience · 2026Review
- The NAT10/acCell communication and signaling : CCS · 2026Review
- N4-acetylcytidine modification bridges metabolic reprogramming and immune evasion in cancer: mechanisms and therapeutic implications.Frontiers in immunology · 2026Review
- Identification of a 10-species microbial signature of inflammatory bowel disease by machine learning and external validation.Cell regeneration (London, England) · 2025Article
- Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning.PloS one · 2025Article
- DeepRNAac4C: a hybrid deep learning framework for RNA N4-acetylcytidine site prediction.Frontiers in genetics · 2025Article
- Hybrid representation learning for human mBioinformatics advances · 2025Article
- MLA-ac4C: A N4-Acetylcytidine Site Prediction Model Based on Multi-Layer Attention, Bidirectional GRU, and DenseNet.Current genomics · 2025Article
- The role and mechanism of NAT10-mediated ac4C modification in tumor development and progression.MedComm · 2024Review
- DPNN-ac4C: a dual-path neural network with self-attention mechanism for identification of N4-acetylcytidine (ac4C) in mRNA.Bioinformatics (Oxford, England) · 2024Article
- ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning.Molecular therapy. Nucleic acids · 2024Article
- Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns.Scientific reports · 2024Article
- TransAC4C-a novel interpretable architecture for multi-species identification of N4-acetylcytidine sites in RNA with single-base resolution.Briefings in bioinformatics · 2024Article
- Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach.iScience · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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