ArticleScientific reports2018
Three-dimensional Epigenome Statistical Model: Genome-wide Chromatin Looping Prediction.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- THC-net: an attention-based deep learning model for chromatin compartment prediction from histone modifications.BMC bioinformatics · 2026Article
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.Briefings in bioinformatics · 2024Review
- HiCDiffusion - diffusion-enhanced, transformer-based prediction of chromatin interactions from DNA sequences.BMC genomics · 2024Article
- Article
- Article
- DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.PLoS computational biology · 2022Article
- Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation.Nature communications · 2022Article
- ProbC: joint modeling of epigenome and transcriptome effects in 3D genome.BMC genomics · 2022Article
- preciseTAD: a transfer learning framework for 3D domain boundary prediction at base-pair resolution.Bioinformatics (Oxford, England) · 2022Article
- Hexavalent chromium promotes differential binding of CTCF to its cognate sites in Euchromatin.Epigenetics · 2021Article
- Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine.Biochimica et biophysica acta. Reviews on cancer · 2021Review
- Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.Briefings in bioinformatics · 2021Article
- Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine.Non-coding RNA · 2021Review
- Article
- Predicting Genome Architecture: Challenges and Solutions.Frontiers in genetics · 2020Review
- Exploring 3D chromatin contacts in gene regulation: The evolution of approaches for the identification of functional enhancer-promoter interaction.Computational and structural biotechnology journal · 2020Review
- A machine learning framework for the prediction of chromatin folding inPeerJ. Computer science · 2020Article
- Quantitative prediction of enhancer-promoter interactions.Genome research · 2020Article
- Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine.Biomolecules · 2019Review
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Authors and funding
2 authors.
Funding
Abstract
This study aims to understand through statistical learning the basic biophysical mechanisms behind three-dimensional folding of epigenomes. The 3DEpiLoop algorithm predicts three-dimensional chromatin looping interactions within topologically associating domains (TADs) from one-dimensional epigenomics and transcription factor profiles using the statistical learning. The predictions obtained by 3DEpiLoop are highly consistent with the reported experimental interactions. The complex signatures of epigenomic and transcription factors within the physically interacting chromatin regions (anchors) are similar across all genomic scales: genomic domains, chromosomal territories, cell types, and different individuals. We report the most important epigenetic and transcription factor features used for interaction identification either shared, or unique for each of sixteen (16) cell lines. The analysis shows that CTCF interaction anchors are enriched by transcription factors yet deficient in histone modifications, while the opposite is true in the case of RNAP II mediated interactions. The code is available at the repository https://bitbucket.org/4dnucleome/3depiloop .
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