ArticleFrontiers in genetics2020
SRHiC: A Deep Learning Model to Enhance the Resolution of Hi-C Data.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Overcoming Artificial Structures in Resolution-Enhanced Hi-C Data by Signal Decomposition and Multi-Scale Attention.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- HiCP2GAN: A Plug and Play Foundation Model-based GAN for Hi-C Enhancement.bioRxiv : the preprint server for biology · 2026Article
- A comprehensive benchmark of single-cell Hi-C embedding tools.Nature communications · 2025Article
- 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
- Unicorn: enhancing single-cell Hi-C data with blind super-resolution for 3D genome structure reconstruction.Bioinformatics (Oxford, England) · 2025Article
- Review
- Article
- ScHiCAtt: Enhancing single-cell Hi-C data resolution using attention-based models.Computational and structural biotechnology journal · 2025Article
- Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model.Bioinformatics (Oxford, England) · 2024Article
- 3C methods in cancer research: recent advances and future prospects.Experimental & molecular medicine · 2024Review
- Hi-C, a chromatin 3D structure technique advancing the functional genomics of immune cells.Frontiers in genetics · 2024Review
- Hypothesis-driven probabilistic modelling enables a principled perspective of genomic compartments.Nucleic acids research · 2023Article
- DeepLoop robustly maps chromatin interactions from sparse allele-resolved or single-cell Hi-C data at kilobase resolution.Nature genetics · 2022Article
- Application of Hi-C and other omics data analysis in human cancer and cell differentiation research.Computational and structural biotechnology journal · 2021Review
- A machine learning framework for the prediction of chromatin folding inPeerJ. Computer science · 2020Article
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2 authors.
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Abstract
Hi-C data is important for studying chromatin three-dimensional structure. However, the resolution of most existing Hi-C data is generally coarse due to sequencing cost. Therefore, it will be helpful if we can predict high-resolution Hi-C data from low-coverage sequencing data. Here we developed a novel and simple computational method based on deep learning named super-resolution Hi-C (SRHiC) to enhance the resolution of Hi-C data. We verified SRHiC on Hi-C data in human cell line. We also evaluated the generalization power of SRHiC by enhancing Hi-C data resolution in other human and mouse cell types. Results showed that SRHiC outperforms the state-of-the-art methods in accuracy of prediction.
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