ArticleInterdisciplinary sciences, computational life sciences2023
Improving language model of human genome for DNA-protein binding prediction based on task-specific pre-training.
Article in Interdisciplinary sciences, computational life sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- A pretrained biomedical large language model for Persian biomedical text mining.Scientific reports · 2026Article
- A DNABERT based deep learning framework for predicting transcription factor binding sites.Scientific reports · 2026Article
- Fine-tuning a sentence transformer for DNA.BMC bioinformatics · 2025Article
- Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine.FEBS open bio · 2025Review
- Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts.ArXiv · 2025Article
- Deciphering genomic codes using advanced natural language processing techniques: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- A review on the applications of Transformer-based language models for nucleotide sequence analysis.Computational and structural biotechnology journal · 2025Review
- 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
- WSHNN: A Weakly Supervised Hybrid Neural Network for the Identification of DNA-protein Binding Sites.Current computer-aided drug design · 2025Article
- Article
- Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.Journal of medical Internet research · 2024Article
- BertSNR: an interpretable deep learning framework for single-nucleotide resolution identification of transcription factor binding sites based on DNA language model.Bioinformatics (Oxford, England) · 2024Article
- GMean-a semi-supervised GRU and K-mean model for predicting the TF binding site.Scientific reports · 2024Article
- ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction.Briefings in bioinformatics · 2024Article
- iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models.Bioinformatics advances · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
The DNA-protein binding plays a pivotal role in regulating gene expression and evolution, and computational identification of DNA-protein has drawn more and more attention in bioinformatics. Recently, variants of BERT are also used to capture the semantic information of DNA sequences for predicting DNA-protein bindings. In this study, we leverage a task-specific pre-training strategy on BERT using large-scale multi-source DNA-protein binding data and present TFBert. TFBert treats DNA sequences as natural sentences and k-mer nucleotides as words. It can effectively extract upstream and downstream nucleotide context information by pre-training the 690 unlabeled ChIP-seq datasets. Experiments show that the pre-trained model can achieve promising performance on every single dataset in the 690 ChIP-seq datasets after simple fine tuning, especially on small datasets. The average AUC is 94.7%, outperforming existing popular methods. In conclusion, this study provides a variant of BERT based on pre-training and achieved state-of-the-art results in predicting DNA-protein bindings. We believe that TFBert can provide insights into other biological sequence classification problems.
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