ArticleBriefings in bioinformatics2024
BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Porcine MutBERT: a family of lightweight genomic foundation models for functional element prediction in pigs.Briefings in bioinformatics · 2026Article
- Benchmarking genomic foundation models for binary classification of gene fusion breakpoints from DNA sequences.BioData mining · 2026Article
- NyxBind: enhancing deep neural representations for transcription factor binding site prediction via contrastive learning.Briefings in bioinformatics · 2026Article
- A DNABERT based deep learning framework for predicting transcription factor binding sites.Scientific reports · 2026Article
- Deep Learning-Based Identification of Pathogenicity Genes inPlants (Basel, Switzerland) · 2026Article
- Modeling strategies forBioinformatics advances · 2026Article
- BBATProt: a framework predicting biological function with enhanced feature extraction via interpretable deep learning.Briefings in bioinformatics · 2025Article
- Transcriptional activation of MACF1 by NR2F1 drives WNT-mediated focal adhesion and metastasis in lung adenocarcinoma.European journal of medical research · 2025Article
- deepTFBS: Improving within- and Cross-Species Prediction of Transcription Factor Binding Using Deep Multi-Task and Transfer Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- An enhanced BERT model with improved local feature extraction and long-range dependency capture in promoter prediction for hearing loss.PeerJ. Computer science · 2025Article
- Identifying transcription factors with cell-type specific DNA binding signatures.BMC genomics · 2024Article
- Assessing next-generation sequencing-based computational methods for predicting transcriptional regulators with query gene sets.Briefings in bioinformatics · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Transcription factors (TFs) are proteins essential for regulating genetic transcriptions by binding to transcription factor binding sites (TFBSs) in DNA sequences. Accurate predictions of TFBSs can contribute to the design and construction of metabolic regulatory systems based on TFs. Although various deep-learning algorithms have been developed for predicting TFBSs, the prediction performance needs to be improved. This paper proposes a bidirectional encoder representations from transformers (BERT)-based model, called BERT-TFBS, to predict TFBSs solely based on DNA sequences. The model consists of a pre-trained BERT module (DNABERT-2), a convolutional neural network (CNN) module, a convolutional block attention module (CBAM) and an output module. The BERT-TFBS model utilizes the pre-trained DNABERT-2 module to acquire the complex long-term dependencies in DNA sequences through a transfer learning approach, and applies the CNN module and the CBAM to extract high-order local features. The proposed model is trained and tested based on 165 ENCODE ChIP-seq datasets. We conducted experiments with model variants, cross-cell-line validations and comparisons with other models. The experimental results demonstrate the effectiveness and generalization capability of BERT-TFBS in predicting TFBSs, and they show that the proposed model outperforms other deep-learning models. The source code for BERT-TFBS is available at https://github.com/ZX1998-12/BERT-TFBS.
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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.