ArticleBioinformatics (Oxford, England)2024
DPNN-ac4C: a dual-path neural network with self-attention mechanism for identification of N4-acetylcytidine (ac4C) in mRNA.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Identifying RNA acInterdisciplinary sciences, computational life sciences · 2026Article
- NAT10 inhibition alleviates renal tubular epithelial cell senescence by impeding ac4C acetylation of PAPP-A mRNA in diabetic nephropathy.Cell death & disease · 2026Article
- ac4C modification sites prediction in human mRNA: a complete review.Briefings in bioinformatics · 2026Review
- Review
- Article
- The function of NAT10-driven N4-acetylcytidine modification in cancer: novel insights and potential therapeutic targets.Cell & bioscience · 2025Review
- MLA-ac4C: A N4-Acetylcytidine Site Prediction Model Based on Multi-Layer Attention, Bidirectional GRU, and DenseNet.Current genomics · 2025Article
- DeepRNAac4C: a hybrid deep learning framework for RNA N4-acetylcytidine site prediction.Frontiers in genetics · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
motivationThe modification of N4-acetylcytidine (ac4C) in RNA is a conserved epigenetic mark that plays a crucial role in post-transcriptional regulation, mRNA stability, and translation efficiency. Traditional methods for detecting ac4C modifications are laborious and costly, necessitating the development of efficient computational approaches for accurate identification of ac4C sites in mRNA.
resultsWe present DPNN-ac4C, a dual-path neural network with a self-attention mechanism for the identification of ac4C sites in mRNA. Our model integrates embedding modules, bidirectional GRU networks, convolutional neural networks, and self-attention to capture both local and global features of RNA sequences. Extensive evaluations demonstrate that DPNN-ac4C outperforms existing models, achieving an AUROC of 91.03%, accuracy of 82.78%, MCC of 65.78%, and specificity of 84.78% on an independent test set. Moreover, DPNN-ac4C exhibits robustness under the Fast Gradient Method attack, maintaining a high level of accuracy in practical applications. AVAILABILITY AND IMPLEMENTATION: The model code and dataset are publicly available on GitHub (https://github.com/shock1ng/DPNN-ac4C).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.