ArticleBMC bioinformatics2023
MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- EnDeep4mC predicts DNAGenome research · 2026Article
- Prediction of DNA N4-Methylcytosine Sites Based on a Position-Aligned Multi-branch Fusion Network.Computational and structural biotechnology journal · 2026Article
- A multi-scale CNN-GRU fusion model with stationary wavelet transform for 14-day ahead dam water level prediction.Scientific reports · 2025Article
- DeepRNAac4C: a hybrid deep learning framework for RNA N4-acetylcytidine site prediction.Frontiers in genetics · 2025Article
- GBMPhos: A Gating Mechanism and Bi-GRU-Based Method for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.Biology · 2024Article
- PSATF-6mA: an integrated learning fusion feature-encoded DNA-6 mA methylcytosine modification site recognition model based on attentional mechanisms.Frontiers in genetics · 2024Article
- Biochemical and structural characterization of the first-discovered metazoan DNA cytosine-N4 methyltransferase from the bdelloid rotifer Adineta vaga.The Journal of biological chemistry · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
N4-methylcytosine (4mC) is an important epigenetic mechanism, which regulates many cellular processes such as cell differentiation and gene expression. The knowledge about the 4mC sites is a key foundation to exploring its roles. Due to the limitation of techniques, precise detection of 4mC is still a challenging task. In this paper, we presented a multi-scale convolution neural network (CNN) and adaptive embedding-based computational method for predicting 4mC sites in mouse genome, which was referred to as MultiScale-CNN-4mCPred. The MultiScale-CNN-4mCPred used adaptive embedding to encode nucleotides, and then utilized multi-scale CNNs as well as long short-term memory to extract more in-depth local properties and contextual semantics in the sequences. The MultiScale-CNN-4mCPred is an end-to-end learning method, which requires no sophisticated feature design. The MultiScale-CNN-4mCPred reached an accuracy of 81.66% in the 10-fold cross-validation, and an accuracy of 84.69% in the independent test, outperforming state-of-the-art methods. We implemented the proposed method into a user-friendly web application which is freely available at: http://www.biolscience.cn/MultiScale-CNN-4mCPred/ .
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Registered trials
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