ReviewBriefings in bioinformatics2024
A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.
Review in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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.
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Who cites it
13 citing papers in PubMed.
- Histone Modifications in Psychiatry: From Canonical Marks to Neurotransmitter-Mediated Epigenetic Signatures.Alpha psychiatry · 2026Review
- Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions.NAR genomics and bioinformatics · 2026Article
- 3D chromatin architecture in cancer: mechanisms of dysregulation and emerging therapeutic strategies.Experimental & molecular medicine · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- Chemical biology tools for studying histone post-translational modifications.Cell chemical biology · 2026Review
- Investigating Phase Separation in Genome Folding via Multiscale Computational Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- CCDC137: A key hub for RNA and epigenetic regulation in cancer research (Review).International journal of oncology · 2026Review
- Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine.World journal of gastroenterology · 2026Review
- Polymer-derived distance penalties improve chromatin interaction predictions from single-cell data across crop genomes.bioRxiv : the preprint server for biology · 2025Article
- siRNA Features-Automated Machine Learning of 3D Molecular Fingerprints and Structures for Therapeutic Off-Target Data.International journal of molecular sciences · 2025Article
- Functional Annotation of De Novo Variants Found Near GWAS Loci Associated With Cleft Lip With or Without Cleft Palate.Birth defects research · 2025Article
- Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.Current issues in molecular biology · 2025Review
- Machine learning tools for deciphering the regulatory logic of enhancers in health and disease.Frontiers in genetics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin interactions in gene expression regulation, which is crucial for understanding molecular mechanisms in biological processes. High-throughput technologies like ChIA-PET, Hi-C, and their derivatives methods have greatly enhanced our knowledge of 3D chromatin architecture. However, the chromatin interaction mechanisms remain largely unexplored. Deep learning, with its powerful feature extraction and pattern recognition capabilities, offers a promising approach for integrating multi-omics data, to build accurate predictive models of chromatin interaction matrices. This review systematically summarizes recent advances in chromatin interaction matrix prediction models. By integrating DNA sequences and epigenetic signals, we investigate the latest developments in these methods. This article details various models, focusing on how one-dimensional (1D) information transforms into the 3D structure chromatin interactions, and how the integration of different deep learning modules specifically affects model accuracy. Additionally, we discuss the critical role of DNA sequence information and epigenetic markers in shaping 3D genome interaction patterns. Finally, this review addresses the challenges in predicting chromatin interaction matrices, in order to improve the precise mapping of chromatin interaction matrices and DNA sequence, and supporting the transformation and theoretical development of 3D genomics across biological systems.
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What Socratic 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 Socratic graph.