ArticleGenome biology2022
iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
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74 citing papers in PubMed.
- Comprehensive evaluation of structural variation detection for germline and somatic analysis with long-read sequencing data.Briefings in bioinformatics · 2026Article
- A token-pruning framework enables efficient representation of the human genome for RNA modification analysis.Bioinformatics (Oxford, England) · 2026Article
- Decoding the sequence determinants of locus-specific DNA methylation across human tissues.Nature communications · 2026Article
- HMA-GCA: hybrid manifold augmentation and gated cross-attention for circRNA-miRNA interaction prediction.Bioinformatics (Oxford, England) · 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
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- A robust computational framework for methylation age and disease-risk prediction based on pairwise learning.Nature computational science · 2026Article
- CMC-WDTK: CpG methylation change prediction by a weight-sharing dual-branch Transformer-Kolmogorov-Arnold network model.BMC genomics · 2026Article
- Bioinformatics insights into plant genomic imprinting: approaches, challenges, and future perspectives.Briefings in functional genomics · 2026Review
- A comprehensive survey of genome language models in bioinformatics.Briefings in bioinformatics · 2026Review
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- Large-scale multi-omic biosequence transformers for modeling protein-nucleic acid interactions.PloS one · 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
- PathoRM: Computational inference of pathogenic RNA methylation sites by incorporating multi-view features.PLoS computational biology · 2025Article
- DMRU: generative deep learning to unravel condition-specific cytosine methylation in plants.Briefings in bioinformatics · 2025Article
- Fine-tuning a sentence transformer for DNA.BMC bioinformatics · 2025Article
- iPro-MP: a BERT-based model to predict multiple prokaryotic promoters.Genome biology · 2025Article
- diPaRIS: Dynamic and Interpretable Protein-RNA Interactions Prediction With U-Shaped Network and Novel Structure Encoding.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Genetic Deconvolution of Embryonic and Maternal Cell-Free DNA in Spent Culture Medium of Human Preimplantation Embryo Through Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
14 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
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Abstract
In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation predictions. Importantly, we show the power of deep language learning in capturing both sequential and functional semantics information from background genomes. Moreover, by integrating the interpretable analysis mechanism, we well explain what the model learns, helping us build the mapping from the discovery of important sequential determinants to the in-depth analysis of their biological functions.
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