ArticleCommunications biology2024
scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Clinical observation of early postoperative wear of balafilcon A and innovative first-generation samfilcon a silicone hydrogel bandage contact lenses after SMILE surgery.BMC research notes · 2025Trial
- DyVAESurv: A VAE-Enhanced Model for Dynamic Survival Analysis.Biometrical journal. Biometrische Zeitschrift · 2026Article
- scMOG: A graph neural network method for regulatory relationship-preserving single-cell multi-omics integration.Quantitative biology (Beijing, China) · 2026Article
- Simultaneous modeling of chromatin conformation changes from multiple single-cell interaction maps with ChromMovie.Genome research · 2026Article
- High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND.Genome research · 2026Article
- OTMODE: an optimal transport theory-based framework for identifying differential features in single-cell multi-omics data.Bioinformatics (Oxford, England) · 2026Article
- Recent advances in single-cell bioinformatics for inferring higher-order chromatin contact maps.BMB reports · 2025Review
- A machine learning-based method to optimize the immunogenicity of human leukocyte antigen class I-restricted neoantigens.Briefings in bioinformatics · 2025Article
- Interpretation of RNA Universe and Coding Potential Using IntRNA.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- GRANet: a graph residual attention network for gene regulatory network inference.Briefings in bioinformatics · 2025Article
- Short-term effects of Ahmed valve implantation on ocular biometry and corneal biomechanics in neovascular glaucoma.BMC research notes · 2025Article
- Predicting cell cycle stage from 3D single-cell nuclear-stained images.Life science alliance · 2025Article
- ChromMovie: A Molecular Dynamics Approach for Simultaneous Modeling of Chromatin Conformation Changes from Multiple Single-Cell Hi-C Maps.bioRxiv : the preprint server for biology · 2025Article
- A novel deep learning framework with dynamic tokenization for identifying chromatin interactions along with motif importance investigation.Briefings in bioinformatics · 2025Article
- Modeling within-host Chikungunya virus dynamics with the immune system using semi-analytical approaches.BMC research notes · 2025Article
- Assessing the effectiveness of group motivational interviewing in raising awareness of mobile gaming addiction among medical students: a pilot study.BMC research notes · 2025Article
- scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information.Communications biology · 2024Article
- Integration of Single-Cell RNA and Bulk RNA Sequencing Reveals Cellular Heterogeneity and Identifies Survival-Associated Regulatory Networks in Glioblastoma.IET systems biologyArticle
- MRANet: Multi-Dimensional Residual Attentional Network for Precise Polyp Segmentation.IET systems biologyArticle
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Authors and funding
7 authors.
Funding
Abstract
The emergence of single-cell Hi-C (scHi-C) technology has provided unprecedented opportunities for investigating the intricate relationship between cell cycle phases and the three-dimensional (3D) structure of chromatin. However, accurately predicting cell cycle phases based on scHi-C data remains a formidable challenge. Here, we present scHiCyclePred, a prediction model that integrates multiple feature sets to leverage scHi-C data for predicting cell cycle phases. scHiCyclePred extracts 3D chromatin structure features by incorporating multi-scale interaction information. The comparative analysis illustrates that scHiCyclePred surpasses existing methods such as Nagano_method and CIRCLET across various metrics including accuracy (ACC), F1 score, Precision, Recall, and balanced accuracy (BACC). In addition, we evaluate scHiCyclePred against the previously published CIRCLET using the dataset of complex tissues (Liu_dataset). Experimental results reveal significant improvements with scHiCyclePred exhibiting improvements of 0.39, 0.52, 0.52, and 0.39 over the CIRCLET in terms of ACC, F1 score, Precision, and Recall metrics, respectively. Furthermore, we conduct analyses on three-dimensional chromatin dynamics and gene features during the cell cycle, providing a more comprehensive understanding of cell cycle dynamics through chromatin structure. scHiCyclePred not only offers insights into cell biology but also holds promise for catalyzing breakthroughs in disease research. Access scHiCyclePred on GitHub at https:// github.com/HaoWuLab-Bioinformatics/ scHiCyclePred .
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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 Socratic graph.