ArticleGenome biology2023
Epiphany: predicting Hi-C contact maps from 1D epigenomic signals.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 42 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed.
- Origins and consequences of oncogenic 3D chromatin remodelling.Nature reviews. Cancer · 2026Review
- GenoME: a MoE-based generative model for individualized, multimodal prediction and perturbation of genomic profiles.Nucleic acids research · 2026Article
- A generalizable Hi-C foundation model for chromatin architecture, single-cell and multiomics analysis across species.Nature methods · 2026Article
- UniversalEPI: robust prediction of cell type-specific and differential chromatin interactions from DNA sequence and chromatin accessibility.Nucleic acids research · 2026Article
- GraphChIAr: genome-wide super-resolution reconstruction of protein-mediated remote chromatin interactions by augmenting hi-C interaction maps with multiple ChIP-seq profiles.Briefings in bioinformatics · 2026Article
- Prediction and functional interpretation of inter-chromosomal genome architecture from DNA sequence with TwinC.Nature communications · 2026Article
- Hi-Compass: a depth-aware deep learning framework for predicting cell-type-specific 3D genome organization from single-cell to spatial resolution.Nature communications · 2026Article
- Early feature extraction drives model performance in high-resolution chromatin accessibility prediction.Genome research · 2026Article
- Modeling nascent transcription from chromatin landscape and structure with CLASTER.Genome biology · 2026Article
- ChromNet: A Multi-Task Learning Framework for Cross-Cell Type Prediction of 3D Chromatin Interactions Using Epigenetic Signals.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- Recent advances in single-cell bioinformatics for inferring higher-order chromatin contact maps.BMB reports · 2025Review
- Epigenetics is all you need: A transformer to decode chromatin structural compartments from the epigenome.PLoS computational biology · 2025Article
- Structural variants in the 3D genome as drivers of disease.Nature reviews. Genetics · 2025Review
- DiCARN-DNase: enhancing cell-to-cell Hi-C resolution using dilated cascading ResNet with self-attention and DNase-seq chromatin accessibility data.Bioinformatics (Oxford, England) · 2025Article
- Prediction and functional interpretation of inter-chromosomal genome architecture from DNA sequence with TwinC.bioRxiv : the preprint server for biology · 2025Article
- Genome structure mapping with high-resolution 3D genomics and deep learning.bioRxiv : the preprint server for biology · 2025Article
- Chrombus-XMBD: a graph convolution model predicting 3D-genome from chromatin features.Briefings in bioinformatics · 2025Article
- Genetically regulated eRNA expression predicts chromatin contact frequency and reveals genetic mechanisms at GWAS loci.Nature communications · 2025Article
- Recipes and ingredients for deep learning models of 3D genome folding.Current opinion in genetics & development · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
Recent deep learning models that predict the Hi-C contact map from DNA sequence achieve promising accuracy but cannot generalize to new cell types and or even capture differences among training cell types. We propose Epiphany, a neural network to predict cell-type-specific Hi-C contact maps from widely available epigenomic tracks. Epiphany uses bidirectional long short-term memory layers to capture long-range dependencies and optionally a generative adversarial network architecture to encourage contact map realism. Epiphany shows excellent generalization to held-out chromosomes within and across cell types, yields accurate TAD and interaction calls, and predicts structural changes caused by perturbations of epigenomic signals.
Indexed as
Identifiers
What OpenQuestion holds
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.