ArticleGenome biology2019
Accurate prediction of cell type-specific transcription factor binding.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis that pooled it.
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Who cites it
66 citing papers in PubMed, 1 synthesis or guideline pooled it, 135 citations in OpenAlex.
- Leverage Large-Scale Biological Networks to Decipher the Genetic Basis of Human Diseases Using Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2021Pooled it
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Inferring Gene Regulatory Networks From Single-Cell RNA Sequencing Data by Dual-Role Graph Contrastive Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- EpiXFormer: a cross-attention neural network for predicting cell type-specific transcription factor binding sites.Briefings in bioinformatics · 2026Article
- Modeling strategies forBioinformatics advances · 2026Article
- Disease-linked regulatory DNA variants and homeostatic transcription factors in epidermis.Nature communications · 2025Article
- Chromatin-dependent motif syntax defines differentiation trajectories.Molecular cell · 2025Article
- Transcriptional regulation as a dose-dependent process: insights from transcription factor tuning.Open biology · 2025Review
- Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli.Nature communications · 2025Article
- Unmeasured human transcription factor ChIP-seq data shape functional genomics and demand strategic prioritization.Briefings in functional genomics · 2025Article
- Gaining insights into Alzheimer's disease by predicting chromatin spatial organization.Bioinformatics advances · 2025Article
- Predicting CTCF cell type active binding sites in human genome.Scientific reports · 2024Article
- Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.Nature communications · 2024Article
- Identifying transcription factors with cell-type specific DNA binding signatures.BMC genomics · 2024Article
- Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data.NAR genomics and bioinformatics · 2024Article
- Systematic dissection of sequence features affecting binding specificity of a pioneer factor reveals binding synergy between FOXA1 and AP-1.Molecular cell · 2024Article
- DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 on-target editing efficiency in specific cellular contexts.Bioinformatics (Oxford, England) · 2024Article
- REUNION: transcription factor binding prediction and regulatory association inference from single-cell multi-omics data.Bioinformatics (Oxford, England) · 2024Article
- Downregulation of HNF4A enables transcriptomic reprogramming during the hepatic acute-phase response.Communications biology · 2024Article
- Genomic transcription factor binding site selection is edited by the chromatin remodeling factor CHD4.Nucleic acids research · 2024Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of cell type-specific, in vivo transcription factor binding sites is one of the central challenges in regulatory genomics. Here, we present our approach that earned a shared first rank in the "ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge" in 2017. In post-challenge analyses, we benchmark the influence of different feature sets and find that chromatin accessibility and binding motifs are sufficient to yield state-of-the-art performance. Finally, we provide 682 lists of predicted peaks for a total of 31 transcription factors in 22 primary cell types and tissues and a user-friendly version of our approach, Catchitt, for download.
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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 OpenQuestion graph.