ReviewQuantitative biology (Beijing, China)2023
3D genomic organization in cancers.
Review in Quantitative biology (Beijing, China), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Epigenetic Regulation of Higher-Order Chromatin Structure (HOCS) and Its Implication in Human Diseases.Cancers · 2026Review
- The hierarchical folding dynamics of topologically associating domains during early embryo development.BMC biology · 2025Article
- Three-dimensional genome architecture in intrahepatic cholangiocarcinoma.Cellular oncology (Dordrecht, Netherlands) · 2025Article
- A novel deep learning framework with dynamic tokenization for identifying chromatin interactions along with motif importance investigation.Briefings in bioinformatics · 2025Article
- A 3D Genome Atlas of Genetic Variants and Their Pathological Effects in Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Long-range transcription factor binding sites clustered regions may mediate transcriptional regulation through phase-separation interactions in early human embryo.Computational and structural biotechnology journal · 2024Article
- Effectiveness of machine learning at modeling the relationship between Hi-C data and copy number variation.Quantitative biology (Beijing, China) · 2024Article
- CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection.Nature communications · 2024Article
- Stratifying TAD boundaries pinpoints focal genomic regions of regulation, damage, and repair.Briefings in bioinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The hierarchical three-dimensional (3D) architectures of chromatin play an important role in fundamental biological processes, such as cell differentiation, cellular senescence, and transcriptional regulation. Aberrant chromatin 3D structural alterations often present in human diseases and even cancers, but their underlying mechanisms remain unclear. Results: 3D chromatin structures (chromatin compartment A/B, topologically associated domains, and enhancer-promoter interactions) play key roles in cancer development, metastasis, and drug resistance. Bioinformatics techniques based on machine learning and deep learning have shown great potential in the study of 3D cancer genome. Conclusion: Current advances in the study of the 3D cancer genome have expanded our understanding of the mechanisms underlying tumorigenesis and development. It will provide new insights into precise diagnosis and personalized treatment for cancers.
Indexed as
Identifiers
What OpenQuestion holds
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.