ArticlePeerJ2019
EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- UniChrom: a universal deep learning architecture for cross-scale chromatin interaction prediction.BMC genomics · 2026Article
- ETNet: an interpretable transformer framework for enhancer-enhancer interaction prediction with cross-context transferability.Briefings in bioinformatics · 2025Article
- DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations.BMC bioinformatics · 2025Article
- Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.Briefings in bioinformatics · 2021Article
- A machine learning framework for the prediction of chromatin folding inPeerJ. Computer science · 2020Article
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Authors and funding
3 authors.
Funding
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Abstract
Chromatin contacts between regulatory elements are of crucial importance for the interpretation of transcriptional regulation and the understanding of disease mechanisms. However, existing computational methods mainly focus on the prediction of interactions between enhancers and promoters, leaving enhancer-enhancer (E-E) interactions not well explored. In this work, we develop a novel deep learning approach, named Enhancer-enhancer contacts prediction (EnContact), to predict E-E contacts using genomic sequences as input. We statistically demonstrated the predicting ability of EnContact using training sets and testing sets derived from HiChIP data of seven cell lines. We also show that our model significantly outperforms other baseline methods. Besides, our model identifies finer-mapping E-E interactions from region-based chromatin contacts, where each region contains several enhancers. In addition, we identify a class of hub enhancers using the predicted E-E interactions and find that hub enhancers tend to be active across cell lines. We summarize that our EnContact model is capable of predicting E-E interactions using features automatically learned from genomic sequences.
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Registered trials
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