ArticleGenes2019
HiCNN2: Enhancing the Resolution of Hi-C Data Using an Ensemble of Convolutional Neural Networks.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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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
16 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
- Unicorn: enhancing single-cell Hi-C data with blind super-resolution for 3D genome structure reconstruction.Bioinformatics (Oxford, England) · 2025Article
- Uncovering topologically associating domains from three-dimensional genome maps with TADGATE.Nucleic acids research · 2025Article
- ScHiCAtt: Enhancing single-cell Hi-C data resolution using attention-based models.Computational and structural biotechnology journal · 2025Article
- Deep neural network models for cell type prediction based on single-cell Hi-C data.BMC genomics · 2024Article
- Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model.Bioinformatics (Oxford, England) · 2024Article
- Learning Micro-C from Hi-C with diffusion models.PLoS computational biology · 2024Article
- Hi-C, a chromatin 3D structure technique advancing the functional genomics of immune cells.Frontiers in genetics · 2024Review
- A Comprehensive Evaluation of Generalizability of Deep Learning-Based Hi-C Resolution Improvement Methods.Genes · 2023Article
- HiC4D: forecasting spatiotemporal Hi-C data with residual ConvLSTM.Briefings in bioinformatics · 2023Article
- 3D genomic organization in cancers.Quantitative biology (Beijing, China) · 2023Review
- Considerations and caveats for analyzing chromatin compartments.Frontiers in molecular biosciences · 2023Review
- DeepLoop robustly maps chromatin interactions from sparse allele-resolved or single-cell Hi-C data at kilobase resolution.Nature genetics · 2022Article
- A systematic evaluation of Hi-C data enhancement methods for enhancing PLAC-seq and HiChIP data.Briefings in bioinformatics · 2022Article
- Application of Hi-C and other omics data analysis in human cancer and cell differentiation research.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We present a deep-learning package named HiCNN2 to learn the mapping between low-resolution and high-resolution Hi-C (a technique for capturing genome-wide chromatin interactions) data, which can enhance the resolution of Hi-C interaction matrices. The HiCNN2 package includes three methods each with a different deep learning architecture: HiCNN2-1 is based on one single convolutional neural network (ConvNet); HiCNN2-2 consists of an ensemble of two different ConvNets; and HiCNN2-3 is an ensemble of three different ConvNets. Our evaluation results indicate that HiCNN2-enhanced high-resolution Hi-C data achieve smaller mean squared error and higher Pearson's correlation coefficients with experimental high-resolution Hi-C data compared with existing methods HiCPlus and HiCNN. Moreover, all of the three HiCNN2 methods can recover more significant interactions detected by Fit-Hi-C compared to HiCPlus and HiCNN. Based on our evaluation results, we would recommend using HiCNN2-1 and HiCNN2-3 if recovering more significant interactions from Hi-C data is of interest, and HiCNN2-2 and HiCNN if the goal is to achieve higher reproducibility scores between the enhanced Hi-C matrix and the real high-resolution Hi-C matrix.
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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.