ArticleNature methods2020
DeepC: predicting 3D genome folding using megabase-scale transfer learning.
Article in Nature methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 140 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
140 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy and potential covariates of artificial intelligence for diagnosing orthopedic fractures: a systematic literature review and meta-analysis.European radiology · 2022Pooled it
- Spatiotemporal AI-based modeling of gene regulation and variant effects in the developing human brain using Seal.Nature neuroscience · 2026Article
- Single-allele nanoscale mapping of regulatory variants.Nature genetics · 2026Article
- GenoME: a MoE-based generative model for individualized, multimodal prediction and perturbation of genomic profiles.Nucleic acids research · 2026Article
- Integrating heterogeneity into topologically associating domain boundary prediction in large genomic context in human.NAR genomics and bioinformatics · 2026Article
- Chiron3D: an interpretable deep learning framework for understanding the DNA code of chromatin looping.Bioinformatics (Oxford, England) · 2026Article
- A generalizable Hi-C foundation model for chromatin architecture, single-cell and multiomics analysis across species.Nature methods · 2026Article
- Genome evolution through polyploidy: Enhancing plant stress resilience in agriculture.Proceedings of the National Academy of Sciences of the United States of America · 2026Review
- Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions.NAR genomics and bioinformatics · 2026Article
- Physics-Based Modeling of Sparse Single-Cell Hi-C Uncovers Structural and Epigenetic Variability.International journal of molecular sciences · 2026Article
- UniversalEPI: robust prediction of cell type-specific and differential chromatin interactions from DNA sequence and chromatin accessibility.Nucleic acids research · 2026Article
- BandHiC: a memory-efficient and user-friendly Python package for organizing and analyzing Hi-C matrices down to sub-kilobase resolution.BMC genomics · 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
- Accurate prediction of cohesin and RNA Polymerase II-associated chromatin interactions using convolutional neural networks.Nucleic acids research · 2026Article
- TF-loop: deciphering the transcription factor regulatory language for CTCF-mediated chromatin loop based on BERT.Briefings in bioinformatics · 2026Article
- Parameter-efficient fine-tuning enables scalable transfer of regulatory sequence models to novel contexts.Genome biology · 2026Article
- Deciphering the 3D genome organization across species from Hi-C data.Nucleic acids research · 2026Article
- Whole Genome Sequencing of Discordant Monozygotic Twins Reveals Regulatory Variants Associated With Nonsyndromic Orofacial Clefts.Human mutation · 2026Article
80 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
Abstract
Predicting the impact of noncoding genetic variation requires interpreting it in the context of three-dimensional genome architecture. We have developed deepC, a transfer-learning-based deep neural network that accurately predicts genome folding from megabase-scale DNA sequence. DeepC predicts domain boundaries at high resolution, learns the sequence determinants of genome folding and predicts the impact of both large-scale structural and single base-pair variations.
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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.