ArticleBioinformatics (Oxford, England)2021
EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- HiCP2GAN: A Plug and Play Foundation Model-based GAN for Hi-C Enhancement.bioRxiv : the preprint server for biology · 2026Article
- SHICEDO: single-cell Hi-C data enhancement with reduced over-smoothing.Bioinformatics (Oxford, England) · 2025Article
- Unicorn: enhancing single-cell Hi-C data with blind super-resolution for 3D genome structure reconstruction.Bioinformatics (Oxford, England) · 2025Article
- Review
- Article
- COCOA: A Framework for Fine-scale Mapping of Cell-type-specific Chromatin Compartments Using Epigenomic Information.Genomics, proteomics & bioinformatics · 2025Article
- Hi-C, a chromatin 3D structure technique advancing the functional genomics of immune cells.Frontiers in genetics · 2024Review
- DFHiC: a dilated full convolution model to enhance the resolution of Hi-C data.Bioinformatics (Oxford, England) · 2023Article
- GILoop: Robust chromatin loop calling across multiple sequencing depths on Hi-C data.iScience · 2022Article
- A systematic evaluation of Hi-C data enhancement methods for enhancing PLAC-seq and HiChIP data.Briefings in bioinformatics · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
motivationThe high-throughput chromosome conformation capture (Hi-C) technique has enabled genome-wide mapping of chromatin interactions. However, high-resolution Hi-C data requires costly, deep sequencing; therefore, it has only been achieved for a limited number of cell types. Machine learning models based on neural networks have been developed as a remedy to this problem.
resultsIn this work, we propose a novel method, EnHiC, for predicting high-resolution Hi-C matrices from low-resolution input data based on a generative adversarial network (GAN) framework. Inspired by non-negative matrix factorization, our model fully exploits the unique properties of Hi-C matrices and extracts rank-1 features from multi-scale low-resolution matrices to enhance the resolution. Using three human Hi-C datasets, we demonstrated that EnHiC accurately and reliably enhanced the resolution of Hi-C matrices and outperformed other GAN-based models. Moreover, EnHiC-predicted high-resolution matrices facilitated the accurate detection of topologically associated domains and fine-scale chromatin interactions. AVAILABILITY AND IMPLEMENTATION: EnHiC is publicly available at https://github.com/wmalab/EnHiC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.