ArticleMolecular therapy. Nucleic acids2020
Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information.
Article in Molecular therapy. Nucleic acids, 2020. 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.
- Epigenetic dysregulation in osteonecrosis of the femoral head: a critical review of DNA methylation, histone modifications, and clinical translation.Journal of orthopaedic surgery and research · 2026Review
- Identifying the DNA methylation preference of transcription factors using ProtBERT and SVM.PLoS computational biology · 2025Article
- TFProtBert: Detection of Transcription Factors Binding to Methylated DNA Using ProtBert Latent Space Representation.International journal of molecular sciences · 2025Article
- Improving prediction performance of general protein language model by domain-adaptive pretraining on DNA-binding protein.Nature communications · 2024Article
- Identifying Transcription Factors That Prefer Binding to Methylated DNA Using ReducedACS omega · 2022Article
- A capsule network-based method for identifying transcription factors.Frontiers in microbiology · 2022Article
- iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy.Frontiers in microbiology · 2022Article
- SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information.Computational and structural biotechnology journal · 2022Article
- Identification ofComputational and mathematical methods in medicine · 2022Article
- The Characterization of Structure and Prediction for Aquaporin in Tumour Progression by Machine Learning.Frontiers in cell and developmental biology · 2022Article
- DNA Methylation Level of Transcription Factor Binding Site in the Promoter Region of Acyl-CoA Synthetase Family Member 3 (Pharmacogenomics and personalized medicine · 2022Article
- ReRF-Pred: predicting amyloidogenic regions of proteins based on their pseudo amino acid composition and tripeptide composition.BMC bioinformatics · 2021Article
- Application of Multilayer Network Models in Bioinformatics.Frontiers in genetics · 2021Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcription factors play key roles in cell-fate decisions by regulating 3D genome conformation and gene expression. The traditional view is that methylation of DNA hinders transcription factors binding to them, but recent research has shown that many transcription factors prefer to bind to methylated DNA. Therefore, identifying such transcription factors and understanding their functions is a stepping-stone for studying methylation-mediated biological processes. In this paper, a two-step discriminated method was proposed to recognize transcription factors and their preference for methylated DNA based only on sequences information. In the first step, the proposed model was used to discriminate transcription factors from non-transcription factors. The areas under the curve (AUCs) are 0.9183 and 0.9116, respectively, for the 5-fold cross-validation test and independent dataset test. Subsequently, for the classification of transcription factors that prefer methylated DNA and transcription factors that prefer non-methylated DNA, our model could produce the AUCs of 0.7744 and 0.7356, respectively, for the 5-fold cross-validation test and independent dataset test. Based on the proposed model, a user-friendly web server called TFPred was built, which can be freely accessed at http://lin-group.cn/server/TFPred/.
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