ArticleBioinformatics (Oxford, England)2019
EPIP: a novel approach for condition-specific enhancer-promoter interaction prediction.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- KansformerEPI: a deep learning framework integrating KAN and transformer for predicting enhancer-promoter interactions.Briefings in bioinformatics · 2025Article
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Article
- A computational modeling of pri-miRNA expression.PloS one · 2024Article
- Integrative approaches based on genomic techniques in the functional studies on enhancers.Briefings in bioinformatics · 2023Review
- Computational methods for identifying enhancer-promoter interactions.Quantitative biology (Beijing, China) · 2023Review
- A systematic study of HIF1A cofactors in hypoxic cancer cells.Scientific reports · 2022Article
- A systematic evaluation of the computational tools for ligand-receptor-based cell-cell interaction inference.Briefings in functional genomics · 2022Article
- A revisit to universal single-copy genes in bacterial genomes.Scientific reports · 2022Article
- A deep learning method for miRNA/isomiR target detection.Scientific reports · 2022Article
- A systematic study of motif pairs that may facilitate enhancer-promoter interactions.Journal of integrative bioinformatics · 2022Article
- EPIsHilbert: Prediction of Enhancer-Promoter Interactions via Hilbert Curve Encoding and Transfer Learning.Genes · 2021Article
- Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.Briefings in bioinformatics · 2021Article
- Interpretation of deep learning in genomics and epigenomics.Briefings in bioinformatics · 2021Review
- An intriguing characteristic of enhancer-promoter interactions.BMC genomics · 2021Article
- Data integration for inferring context-specific gene regulatory networks.Current opinion in systems biology · 2020Article
- Shared distal regulatory regions may contribute to the coordinated expression of human ribosomal protein genes.Genomics · 2020Article
- Integrative computational epigenomics to build data-driven gene regulation hypotheses.GigaScience · 2020Article
- Exploring 3D chromatin contacts in gene regulation: The evolution of approaches for the identification of functional enhancer-promoter interaction.Computational and structural biotechnology journal · 2020Review
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Authors and funding
4 authors.
Funding
Abstract
motivationThe identification of enhancer-promoter interactions (EPIs), especially condition-specific ones, is important for the study of gene transcriptional regulation. Existing experimental approaches for EPI identification are still expensive, and available computational methods either do not consider or have low performance in predicting condition-specific EPIs.
resultsWe developed a novel computational method called EPIP to reliably predict EPIs, especially condition-specific ones. EPIP is capable of predicting interactions in samples with limited data as well as in samples with abundant data. Tested on more than eight cell lines, EPIP reliably identifies EPIs, with an average area under the receiver operating characteristic curve of 0.95 and an average area under the precision-recall curve of 0.73. Tested on condition-specific EPIPs, EPIP correctly identified 99.26% of them. Compared with two recently developed methods, EPIP outperforms them with a better accuracy. AVAILABILITY AND IMPLEMENTATION: The EPIP tool is freely available at http://www.cs.ucf.edu/˜xiaoman/EPIP/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.