ArticleiScience2020
iDNA-MS: An Integrated Computational Tool for Detecting DNA Modification Sites in Multiple Genomes.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed.
- MOSAIC enables selective transfer and fairness-aware generalization in cross-species DNA methylation prediction.Nucleic acids research · 2026Article
- MeDiCNet: Integrating Multi-scale Dynamic Convolution and Enhanced Position-Aware Transformer for DNA Methylation Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- EnDeep4mC predicts DNAGenome research · 2026Article
- Mamba6mA: a Mamba-based DNA N6-methyladenine site prediction model.Bioinformatics (Oxford, England) · 2026Article
- Improving B-cell Linear Epitope PredictionCurrent drug targets · 2026Article
- Benchmarking DNA foundation models for genomic and genetic tasks.Nature communications · 2025Article
- iDNA-DAPHA: a generic framework for methylation prediction via domain-adaptive pretraining and hierarchical attention.Briefings in bioinformatics · 2025Article
- DMRU: generative deep learning to unravel condition-specific cytosine methylation in plants.Briefings in bioinformatics · 2025Article
- From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling.Briefings in bioinformatics · 2025Review
- PlantEMS: A comprehensive database of epigenetic modification sites across multiple plant species.Plant communications · 2025Article
- Methyl-GP: accurate generic DNA methylation prediction based on a language model and representation learning.Nucleic acids research · 2025Article
- An ensemble deep learning framework for multi-class LncRNA subcellular localization with innovative encoding strategy.BMC biology · 2025Article
- Understanding prokaryotic adaptation through advanced DNA methylation detection techniques.The ISME journal · 2025Review
- iResNetDM: An interpretable deep learning approach for four types of DNA methylation modification prediction.Computational and structural biotechnology journal · 2024Article
- The markers of the predictive DNA test for canine hip dysplasia may have a stronger relationship with elbow dysplasia.Heliyon · 2024Article
- DeepPGD: A Deep Learning Model for DNA Methylation Prediction Using Temporal Convolution, BiLSTM, and Attention Mechanism.International journal of molecular sciences · 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
- StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning.BMC genomics · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
5hmC, 6mA, and 4mC are three common DNA modifications and are involved in various of biological processes. Accurate genome-wide identification of these sites is invaluable for better understanding their biological functions. Owing to the labor-intensive and expensive nature of experimental methods, it is urgent to develop computational methods for the genome-wide detection of these sites. Keeping this in mind, the current study was devoted to construct a computational method to identify 5hmC, 6mA, and 4mC. We initially used K-tuple nucleotide component, nucleotide chemical property and nucleotide frequency, and mono-nucleotide binary encoding scheme to formulate samples. Subsequently, random forest was utilized to identify 5hmC, 6mA, and 4mC sites. Cross-validated results showed that the proposed method could produce the excellent generalization ability in the identification of the three modification sites. Based on the proposed model, a web-server called iDNA-MS was established and is freely accessible at http://lin-group.cn/server/iDNA-MS.
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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.