ArticleBriefings in bioinformatics2021
Deep4mC: systematic assessment and computational prediction for DNA N4-methylcytosine sites by deep learning.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- EnDeep4mC predicts DNAGenome research · 2026Article
- Benchmarking DNA foundation models for genomic and genetic tasks.Nature communications · 2025Article
- From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling.Briefings in bioinformatics · 2025Review
- Deciphering RNA modification and post-transcriptional regulation with NetRNApan.Briefings in bioinformatics · 2025Article
- DMRU: generative deep learning to unravel condition-specific cytosine methylation in plants.Briefings in bioinformatics · 2025Article
- DeepRNAac4C: a hybrid deep learning framework for RNA N4-acetylcytidine site prediction.Frontiers in genetics · 2025Article
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- iResNetDM: An interpretable deep learning approach for four types of DNA methylation modification prediction.Computational and structural biotechnology journal · 2024Article
- Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns.Scientific reports · 2024Article
- Unveiling Gene Interactions in Alzheimer's Disease by Integrating Genetic and Epigenetic Data with a Network-Based Approach.Epigenomes · 2024Article
- iDNA-ITLM: An interpretable and transferable learning model for identifying DNA methylation.PloS one · 2024Article
- iDNA-ITLM: An interpretable and transferable learning model for identifying DNA methylation.PloS one · 2024Article
- iDNA-ITLM: An interpretable and transferable learning model for identifying DNA methylation.PloS one · 2024Article
- iDNA-OpenPrompt: OpenPrompt learning model for identifying DNA methylation.Frontiers in genetics · 2024Article
- Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction.BMC genomics · 2023Article
- MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation.Theory in biosciences = Theorie in den Biowissenschaften · 2023Article
- Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.PLoS computational biology · 2023Article
- Comparative evaluation and analysis of DNA N4-methylcytosine methylation sites using deep learning.Frontiers in genetics · 2023Article
- i4mC-GRU: Identifying DNA NComputational and structural biotechnology journal · 2023Article
- MuLan-Methyl-multiple transformer-based language models for accurate DNA methylation prediction.GigaScience · 2022Article
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3 authors.
Funding
Abstract
DNA N4-methylcytosine (4mC) modification represents a novel epigenetic regulation. It involves in various cellular processes, including DNA replication, cell cycle and gene expression, among others. In addition to experimental identification of 4mC sites, in silico prediction of 4mC sites in the genome has emerged as an alternative and promising approach. In this study, we first reviewed the current progress in the computational prediction of 4mC sites and systematically evaluated the predictive capacity of eight conventional machine learning algorithms as well as 12 feature types commonly used in previous studies in six species. Using a representative benchmark dataset, we investigated the contribution of feature selection and stacking approach to the model construction, and found that feature optimization and proper reinforcement learning could improve the performance. We next recollected newly added 4mC sites in the six species' genomes and developed a novel deep learning-based 4mC site predictor, namely Deep4mC. Deep4mC applies convolutional neural networks with four representative features. For species with small numbers of samples, we extended our deep learning framework with a bootstrapping method. Our evaluation indicated that Deep4mC could obtain high accuracy and robust performance with the average area under curve (AUC) values greater than 0.9 in all species (range: 0.9005-0.9722). In comparison, Deep4mC achieved an AUC value improvement from 10.14 to 46.21% when compared to previous tools in these six species. A user-friendly web server (https://bioinfo.uth.edu/Deep4mC) was built for predicting putative 4mC sites in a genome.
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