ArticleBioinformatics (Oxford, England)2019
A simple convolutional neural network for prediction of enhancer-promoter interactions with DNA sequence data.
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 40 papers.
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Who cites it
40 citing papers in PubMed, 97 citations in OpenAlex.
- Deep learning and attention mechanisms to identify key genes and their implications for the origin of insect wings.Scientific reports · 2026Article
- EPINTLM: enhancer-promoter prediction with pretrained k-mer embeddings and residual cross-attention.Briefings in bioinformatics · 2026Article
- EPIFBMC: A New Model for Enhancer-Promoter Interaction Prediction.International journal of molecular sciences · 2025Article
- Class-balanced negative training sets for improving classifier model predictions of enhancer-promoter interactions.BMC bioinformatics · 2025Article
- PCVR: a pre-trained contextualized visual representation for DNA sequence classification.BMC bioinformatics · 2025Article
- EPIPDLF: a pretrained deep learning framework for predicting enhancer-promoter interactions.Bioinformatics (Oxford, England) · 2025Article
- Review
- KansformerEPI: a deep learning framework integrating KAN and transformer for predicting enhancer-promoter interactions.Briefings in bioinformatics · 2025Article
- RAEPI: Predicting Enhancer-Promoter Interactions Based on Restricted Attention Mechanism.Interdisciplinary sciences, computational life sciences · 2025Article
- Inferring protein from transcript abundances using convolutional neural networks.BioData mining · 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
- DeepEPI: CNN-transformer-based model for extracting TF interactions through predicting enhancer-promoter interactions.Bioinformatics advances · 2025Article
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- EPI-DynFusion: enhancer-promoter interaction prediction model based on sequence features and dynamic fusion mechanisms.Frontiers in genetics · 2025Article
- GATv2EPI: Predicting Enhancer-Promoter Interactions with a Dynamic Graph Attention Network.Genes · 2024Article
- EPI-Trans: an effective transformer-based deep learning model for enhancer promoter interaction prediction.BMC bioinformatics · 2024Article
- Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition.Cancers · 2024Review
- Assessing the reliability of point mutation as data augmentation for deep learning with genomic data.BMC bioinformatics · 2024Article
- CrnnCrispr: An Interpretable Deep Learning Method for CRISPR/Cas9 sgRNA On-Target Activity Prediction.International journal of molecular sciences · 2024Article
- Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationEnhancer-promoter interactions (EPIs) in the genome play an important role in transcriptional regulation. EPIs can be useful in boosting statistical power and enhancing mechanistic interpretation for disease- or trait-associated genetic variants in genome-wide association studies. Instead of expensive and time-consuming biological experiments, computational prediction of EPIs with DNA sequence and other genomic data is a fast and viable alternative. In particular, deep learning and other machine learning methods have been demonstrated with promising performance.
resultsFirst, using a published human cell line dataset, we demonstrate that a simple convolutional neural network (CNN) performs as well as, if no better than, a more complicated and state-of-the-art architecture, a hybrid of a CNN and a recurrent neural network. More importantly, in spite of the well-known cell line-specific EPIs (and corresponding gene expression), in contrast to the standard practice of training and predicting for each cell line separately, we propose two transfer learning approaches to training a model using all cell lines to various extents, leading to substantially improved predictive performance. AVAILABILITY AND IMPLEMENTATION: Computer code is available at https://github.com/zzUMN/Combine-CNN-Enhancer-and-Promoters. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.