ArticleNucleic acids research2018
A novel method for improved accuracy of transcription factor binding site prediction.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 56 citations in OpenAlex.
- OptimDase: An Algorithm for Predicting DNA Binding Sites with Combined Feature Encoding.Interdisciplinary sciences, computational life sciences · 2025Article
- Review
- 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
- Enhancer reprogramming: critical roles in cancer and promising therapeutic strategies.Cell death discovery · 2025Review
- Profiling conserved transcription factor binding motifs in Phaseolus vulgaris through comparative genomics.BMC genomics · 2025Article
- Abundant repressor binding sites in human enhancers are associated with the fine-tuning of gene regulation.iScience · 2025Article
- BADDADAN: Mechanistic modelling of time-series gene module expression.Quantitative plant biology · 2025Article
- The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes.BMC bioinformatics · 2024Article
- HOCOMOCO in 2024: a rebuild of the curated collection of binding models for human and mouse transcription factors.Nucleic acids research · 2024Article
- Multiomics-integrated deep language model enables in silico genome-wide detection of transcription factor binding site in unexplored biosamples.Bioinformatics (Oxford, England) · 2024Article
- Recent advances in exploring transcriptional regulatory landscape of crops.Frontiers in plant science · 2024Review
- Characterizing the SREB G protein-coupled receptor family in fish: Brain gene expression and genomic differences in upstream transcription factor binding sites.Comparative biochemistry and physiology. Part A, Molecular & integrative physiology · 2023Article
- Integrative single-nucleus multi-omics analysis prioritizes candidate cis and trans regulatory networks and their target genes in Alzheimer's disease brains.Cell & bioscience · 2023Article
- In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets.Molecules (Basel, Switzerland) · 2022Review
- AcneGrader: An ensemble pruning of the deep learning base models to grade acne.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2022Article
- Plant-DTI: Extending the landscape of TF protein and DNA interaction in plants by a machine learning-based approach.Frontiers in plant science · 2022Article
- DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome.Bioinformatics (Oxford, England) · 2021Article
- Sequence-based GWAS and post-GWAS analyses reveal a key role of SLC37A1, ANKH, and regulatory regions on bovine milk mineral content.Scientific reports · 2021Article
- Whole-genome sequencing reveals insights into the adaptation of French Charolais cattle to Cuban tropical conditions.Genetics, selection, evolution : GSE · 2021Article
- Integrating genome sequence and structural data for statistical learning to predict transcription factor binding sites.Nucleic acids research · 2020Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.
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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 OpenQuestion graph.