ArticleNucleic acids research2019
DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
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Who cites it
74 citing papers in PubMed, 147 citations in OpenAlex.
- Foundation models in omics research: a comprehensive survey.Briefings in bioinformatics · 2026Review
- An overview of self-supervised deep learning applications to molecular data.Briefings in bioinformatics · 2026Review
- PlantCTCIP: Chromatin Interaction Prediction Using Convolutional Neural Network and Transformer in Plants.Plant biotechnology journal · 2026Article
- UniversalEPI: robust prediction of cell type-specific and differential chromatin interactions from DNA sequence and chromatin accessibility.Nucleic acids research · 2026Article
- Unraveling Atherosclerosis through Multi-omics: Systematic Insights into the Unique Applications and Clinical Perspectives.Current atherosclerosis reports · 2026Review
- GraphLooper: predicting chromatin loops based on hierarchical multi-view graph pooling method.Briefings in bioinformatics · 2026Article
- Accurate prediction of cohesin and RNA Polymerase II-associated chromatin interactions using convolutional neural networks.Nucleic acids research · 2026Article
- CMC-WDTK: CpG methylation change prediction by a weight-sharing dual-branch Transformer-Kolmogorov-Arnold network model.BMC genomics · 2026Article
- A scalable computational framework for predicting gene expression from candidateGenome research · 2026Article
- Deep learning for regulatory genomics: a survey of models, challenges, and applications.Bioinformatics advances · 2026Review
- ETNet: an interpretable transformer framework for enhancer-enhancer interaction prediction with cross-context transferability.Briefings in bioinformatics · 2025Article
- CREATE: cell-type-specific cis-regulatory element identification via discrete embedding.Nature communications · 2025Article
- Review
- DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations.BMC bioinformatics · 2025Article
- Genetically regulated eRNA expression predicts chromatin contact frequency and reveals genetic mechanisms at GWAS loci.Nature communications · 2025Article
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- EpiGePT: a pretrained transformer-based language model for context-specific human epigenomics.Genome biology · 2024Article
- GATv2EPI: Predicting Enhancer-Promoter Interactions with a Dynamic Graph Attention Network.Genes · 2024Article
- HiCDiffusion - diffusion-enhanced, transformer-based prediction of chromatin interactions from DNA sequences.BMC genomics · 2024Article
- Polymer Physics Models Reveal Structural Folding Features of Single-Molecule Gene Chromatin Conformations.International journal of molecular sciences · 2024Article
14 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Interactions between regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanisms. Hi-C technique has been developed for genome-wide detection of chromatin contacts. However, unless extremely deep sequencing is performed on a very large number of input cells, which is technically limited and expensive, current Hi-C experiments do not have high enough resolution to resolve contacts between regulatory elements. Here, we develop DeepTACT, a bootstrapping deep learning model, to integrate genome sequences and chromatin accessibility data for the prediction of chromatin contacts between regulatory elements. DeepTACT can infer not only promoter-enhancer interactions, but also promoter-promoter interactions. In tests based on promoter capture Hi-C data, DeepTACT shows better performance over existing methods. DeepTACT analysis also identifies a class of hub promoters, which are correlated with transcriptional activation across cell lines, enriched in housekeeping genes, functionally related to fundamental biological processes, and capable of reflecting cell similarity. Finally, the utility of chromatin contacts in the study of human diseases is illustrated by the association of IFNA2 to coronary artery disease via an integrative analysis of GWAS data and interactions predicted by DeepTACT.
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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.