ArticleGenes2021
i4mC-Deep: An Intelligent Predictor of N4-Methylcytosine Sites Using a Deep Learning Approach with Chemical Properties.
Article in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
- Efficient and interpretable DNA/RNA representation using Komlós-Hadamard transforms.BMC bioinformatics · 2026Article
- Occurrence, Properties, Applications and Analytics of Cytosine and Its Derivatives.Molecules (Basel, Switzerland) · 2025Review
- Possum: identification and interpretation of potassium ion inhibitors using probabilistic feature vectors.Archives of toxicology · 2025Article
- CpGFuse: a holistic approach for accurate identification of methylation states of DNA CpG sites.Briefings in bioinformatics · 2024Article
- A Novel Repetition Frequency-Based DNA Encoding Scheme to Predict Human and Mouse DNA Enhancers with Deep Learning.Biomimetics (Basel, Switzerland) · 2023Article
- iProm-Sigma54: A CNN Base Prediction Tool forCells · 2023Article
- Article
- i4mC-GRU: Identifying DNA NComputational and structural biotechnology journal · 2023Article
- Systematic Analysis and Accurate Identification of DNA N4-Methylcytosine Sites by Deep Learning.Frontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA is subject to epigenetic modification by the molecule N4-methylcytosine (4mC). N4-methylcytosine plays a crucial role in DNA repair and replication, protects host DNA from degradation, and regulates DNA expression. However, though current experimental techniques can identify 4mC sites, such techniques are expensive and laborious. Therefore, computational tools that can predict 4mC sites would be very useful for understanding the biological mechanism of this vital type of DNA modification. Conventional machine-learning-based methods rely on hand-crafted features, but the new method saves time and computational cost by making use of learned features instead. In this study, we propose i4mC-Deep, an intelligent predictor based on a convolutional neural network (CNN) that predicts 4mC modification sites in DNA samples. The CNN is capable of automatically extracting important features from input samples during training. Nucleotide chemical properties and nucleotide density, which together represent a DNA sequence, act as CNN input data. The outcome of the proposed method outperforms several state-of-the-art predictors. When i4mC-Deep was used to analyze
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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.