ArticleNucleic acids research2025
Methyl-GP: accurate generic DNA methylation prediction based on a language model and representation learning.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Integrated Transcriptomic Analyses Identify Four Prognosis-Associated Genes in Hepatocellular Carcinoma.International journal of molecular sciences · 2026Article
- MOSAIC enables selective transfer and fairness-aware generalization in cross-species DNA methylation prediction.Nucleic acids research · 2026Article
- EssTFNet: integration of adaptive time-frequency and DNA language models for interpretable human essential gene prediction.Briefings in bioinformatics · 2026Article
- Comprehensive identification of three key genes and three key DNA methylation CpG sites associated with prognosis and prediction in colon adenocarcinoma.Discover oncology · 2026Article
- SpaMFG: a spatial multi-omics integration method based on feature grouping.Bioinformatics (Oxford, England) · 2026Article
- KmalPred: a deep learning framework for lysine malonylation site prediction using protein language model representations.BMC biology · 2026Article
- ac4C modification sites prediction in human mRNA: a complete review.Briefings in bioinformatics · 2026Review
- HybridGNN: a graph neural network approach for human miRNA-disease association prediction.Bioinformatics (Oxford, England) · 2026Article
- HNRM: hyperedge neighborhood-based representation for predicting N6-methyladenosine-related regulatory pathways.BMC biology · 2026Article
- A robust computational framework for methylation age and disease-risk prediction based on pairwise learning.Nature computational science · 2026Article
- MsgaBpred: A B-cell epitope predictor integrating AlphaFold3-predicted structures with multi-scale GCNs and pre-trained language model ESM-C.PLoS computational biology · 2026Article
- CMC-WDTK: CpG methylation change prediction by a weight-sharing dual-branch Transformer-Kolmogorov-Arnold network model.BMC genomics · 2026Article
- FKSUDDAPre: A drug-disease association prediction framework based on F-TEST feature selection and AMDKSU resampling with interpretability analysis.PLoS computational biology · 2026Article
- Aegis: a transformer-based deep learning framework for the accurate identification of anticancer peptides.BMC biology · 2026Article
- BiGKbhb: a bi-directional gated recurrent unit model for predicting lysine β-hydroxybutyrylation sites.BMC genomics · 2026Article
- Bioinformatics insights into plant genomic imprinting: approaches, challenges, and future perspectives.Briefings in functional genomics · 2026Review
- DFL-MHC: MHC identification model based on dual-stage training and multi-view feature fusion.Frontiers in genetics · 2026Article
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- ProjFusNet: deep neural network for peptide precursor prediction using projection-fused protein language model and structural features.Journal of cheminformatics · 2025Article
- Navigating the 3D genome at single-cell resolution: techniques, computation, and mechanistic landscapes.Briefings in bioinformatics · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Accurate prediction of DNA methylation remains a challenge. Identifying DNA methylation is important for understanding its functions and elucidating its role in gene regulation mechanisms. In this study, we propose Methyl-GP, a general predictor that accurately predicts three types of DNA methylation from DNA sequences. We found that the conservation of sequence patterns among different species contributes to enhancing the generalizability of the model. By fine-tuning a language model on a dataset comprising multiple species with similar sequence patterns and employing a fusion module to integrate embeddings into a high-quality comprehensive representation, Methyl-GP demonstrates satisfactory predictive performance in methylation identification. Experiments on 17 benchmark datasets for three types of DNA methylation (4mC, 5hmC, and 6mA) demonstrate the superiority of Methyl-GP over existing predictors. Furthermore, by utilizing the attention mechanism, we have visualized the sequence patterns learned by the model, which may help us to gain a deeper understanding of methylation patterns across various species.
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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 Socratic graph.