ArticleBriefings in bioinformatics2022
BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- Mamba6mA: a Mamba-based DNA N6-methyladenine site prediction model.Bioinformatics (Oxford, England) · 2026Article
- Large-scale multi-omic biosequence transformers for modeling protein-nucleic acid interactions.PloS one · 2026Article
- DNA Methylation Recognition Using Hybrid Deep Learning with Dual Nucleotide Visualization Fusion Feature Encoding.Interdisciplinary sciences, computational life sciences · 2025Article
- Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine.FEBS open bio · 2025Review
- Large-Scale Multi-omic Biosequence Transformers for Modeling Protein-Nucleic Acid Interactions.ArXiv · 2025Article
- Methyl-GP: accurate generic DNA methylation prediction based on a language model and representation learning.Nucleic acids research · 2025Article
- Review
- HyenaCircle: a HyenaDNA-based pretrained large language model for long eccDNA 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
- Deep5mC: Predicting 5-methylcytosine (5mC) methylation status using a deep learning transformer approach.Computational and structural biotechnology journal · 2025Article
- Article
- Application of artificial intelligence large language models in drug target discovery.Frontiers in pharmacology · 2025Review
- iResNetDM: An interpretable deep learning approach for four types of DNA methylation modification prediction.Computational and structural biotechnology journal · 2024Article
- MFPSP: Identification of fungal species-specific phosphorylation site using offspring competition-based genetic algorithm.PLoS computational biology · 2024Article
- Multiple sequence alignment-based RNA language model and its application to structural inference.Nucleic acids research · 2024Article
- iDNA-OpenPrompt: OpenPrompt learning model for identifying DNA methylation.Frontiers in genetics · 2024Article
- PSATF-6mA: an integrated learning fusion feature-encoded DNA-6 mA methylcytosine modification site recognition model based on attentional mechanisms.Frontiers in genetics · 2024Article
- STM-ac4C: a hybrid model for identification of N4-acetylcytidine (ac4C) in human mRNA based on selective kernel convolution, temporal convolutional network, and multi-head self-attention.Frontiers in genetics · 2024Article
- iDNA-ITLM: An interpretable and transferable learning model for identifying DNA methylation.PloS one · 2024Article
Corrections and comments
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4 authors.
Funding
Abstract
N6-methyladenine (6mA) is associated with important roles in DNA replication, DNA repair, transcription, regulation of gene expression. Several experimental methods were used to identify DNA modifications. However, these experimental methods are costly and time-consuming. To detect the 6mA and complement these shortcomings of experimental methods, we proposed a novel, deep leaning approach called BERT6mA. To compare the BERT6mA with other deep learning approaches, we used the benchmark datasets including 11 species. The BERT6mA presented the highest AUCs in eight species in independent tests. Furthermore, BERT6mA showed higher and comparable performance with the state-of-the-art models while the BERT6mA showed poor performances in a few species with a small sample size. To overcome this issue, pretraining and fine-tuning between two species were applied to the BERT6mA. The pretrained and fine-tuned models on specific species presented higher performances than other models even for the species with a small sample size. In addition to the prediction, we analyzed the attention weights generated by BERT6mA to reveal how the BERT6mA model extracts critical features responsible for the 6mA prediction. To facilitate biological sciences, the BERT6mA online web server and its source codes are freely accessible at https://github.com/kuratahiroyuki/BERT6mA.git, respectively.
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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 Socratic graph.