ScHiCAtt: Enhancing single-cell Hi-C data resolution using attention-based models.

ABSTRACT: The spatial organization of chromatin is fundamental to gene regulation and essential for proper cellular function. The Hi-C technique remains one of the leading methods for unraveling 3D genome structures; however, limited resolution, data sparsity, and incomplete coverage in single-cell Hi-C data pose significant challenges for comprehensive analysis. Traditional convolutional neural network-based models often suffer from blurring and loss of fine details, while generative adversarial network based methods encounter difficulties in maintaining diversity and generalization. Moreover, existing algorithms perform poorly in cross-cell line generalization, where a model trained on one cell type is used to enhance high-resolution data in another cell type. To address these limitations, we prop

SUBMITTER: Menon R 

PROVIDER: S-EPMC11953966 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature