ArticleGenome biology2021
Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences.
Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- PlantCTCIP: Chromatin Interaction Prediction Using Convolutional Neural Network and Transformer in Plants.Plant biotechnology journal · 2026Article
- Predicting enhancer-promoter interactions using a stacking-based ensemble strategy.Bioinformatics (Oxford, England) · 2026Article
- UniversalEPI: robust prediction of cell type-specific and differential chromatin interactions from DNA sequence and chromatin accessibility.Nucleic acids research · 2026Article
- NanoLoop: A Deep Learning Framework Leveraging Nanopore Sequencing for Chromatin Loop Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Accurate prediction of cohesin and RNA Polymerase II-associated chromatin interactions using convolutional neural networks.Nucleic acids research · 2026Article
- TF-loop: deciphering the transcription factor regulatory language for CTCF-mediated chromatin loop based on BERT.Briefings in bioinformatics · 2026Article
- UniChrom: a universal deep learning architecture for cross-scale chromatin interaction prediction.BMC genomics · 2026Article
- A scalable computational framework for predicting gene expression from candidateGenome research · 2026Article
- CLAMP: predicting specific protein-mediated chromatin loops in diverse species with a chromatin accessibility language model.Genome biology · 2026Article
- Artificial intelligence in haematology laboratory diagnosis: Current applications, challenges, and future directions.African journal of laboratory medicine · 2026Review
- Physical models reveal indirect reader protein interactions that facilitate epigenetic crosstalk.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- ETNet: an interpretable transformer framework for enhancer-enhancer interaction prediction with cross-context transferability.Briefings in bioinformatics · 2025Article
- The emerging sequence grammar of 3D genome organisation.Human genetics · 2025Review
- Article
- Techniques to detect physical interactions between genomic regions.Epigenomics · 2025Review
- EPIPDLF: a pretrained deep learning framework for predicting enhancer-promoter interactions.Bioinformatics (Oxford, England) · 2025Article
- Review
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- GATv2EPI: Predicting Enhancer-Promoter Interactions with a Dynamic Graph Attention Network.Genes · 2024Article
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Authors and funding
12 authors.
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Abstract
Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.
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