ArticleGenome research2020
Quantitative prediction of enhancer-promoter interactions.
Article in Genome research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- Integrating heterogeneity into topologically associating domain boundary prediction in large genomic context in human.NAR genomics and bioinformatics · 2026Article
- Accurate prediction of cohesin and RNA Polymerase II-associated chromatin interactions using convolutional neural networks.Nucleic acids research · 2026Article
- UniChrom: a universal deep learning architecture for cross-scale chromatin interaction prediction.BMC genomics · 2026Article
- Discovering the complete enhancer map of human herpesviruses using a natural language processing model.Nature communications · 2025Article
- EPIFBMC: A New Model for Enhancer-Promoter Interaction Prediction.International journal of molecular sciences · 2025Article
- Class-balanced negative training sets for improving classifier model predictions of enhancer-promoter interactions.BMC bioinformatics · 2025Article
- Charm is a flexible pipeline to simulate chromosomal rearrangements on Hi-C-like data.NAR genomics and bioinformatics · 2025Article
- A novel deep learning framework with dynamic tokenization for identifying chromatin interactions along with motif importance investigation.Briefings in bioinformatics · 2025Article
- Non-local modeling of enhancer-promoter interactions, a correspondence on "LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions".Applied intelligence (Dordrecht, Netherlands) · 2025Article
- Recipes and ingredients for deep learning models of 3D genome folding.Current opinion in genetics & development · 2025Review
- GENA-LM: a family of open-source foundational DNA language models for long sequences.Nucleic acids research · 2025Article
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Review
- GATv2EPI: Predicting Enhancer-Promoter Interactions with a Dynamic Graph Attention Network.Genes · 2024Article
- 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
- Polymer Physics Models Reveal Structural Folding Features of Single-Molecule Gene Chromatin Conformations.International journal of molecular sciences · 2024Article
- Article
- Structural variants in the Epb41l4a locus: TAD disruption and Nrep gene misregulation as hypothetical drivers of neurodevelopmental outcomes.Scientific reports · 2024Article
- Hi-BDiSCO: folding 3D mesoscale genome structures from Hi-C data using brownian dynamics.Nucleic acids research · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent experimental and computational efforts have provided large data sets describing three-dimensional organization of mouse and human genomes and showed the interconnection between the expression profile, epigenetic state, and spatial interactions of loci. These interconnections were utilized to infer the spatial organization of chromatin, including enhancer-promoter contacts, from one-dimensional epigenetic marks. Here, we show that the predictive power of some of these algorithms is overestimated due to peculiar properties of the biological data. We propose an alternative approach, which provides high-quality predictions of chromatin interactions using information on gene expression and CTCF-binding alone. Using multiple metrics, we confirmed that our algorithm could efficiently predict the three-dimensional architecture of both normal and rearranged genomes.
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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.