ArticleScientific reports2016
EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Genomic language model for predicting enhancers and their allele-specific activity in the human genome.Bioinformatics (Oxford, England) · 2026Article
- Advances in Artificial Intelligence (AI) Models and Generative Algorithms Represent a New Paradigm for Genomics Research.International journal of molecular sciences · 2025Review
- DeepRegFinder: deep learning-based regulatory elements finder.Bioinformatics advances · 2024Article
- Integrative approaches based on genomic techniques in the functional studies on enhancers.Briefings in bioinformatics · 2023Review
- PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks.Animals : an open access journal from MDPI · 2023Article
- A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome.Nucleic acids research · 2023Article
- ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.PLoS computational biology · 2021Article
- Simultaneous learning of individual microRNA-gene interactions and regulatory comodules.BMC bioinformatics · 2021Article
- Identification and Characterisation of Putative Enhancer Elements in Mouse Embryonic Stem Cells.Bioinformatics and biology insights · 2021Article
- A pitfall for machine learning methods aiming to predict across cell types.Genome biology · 2020Article
- Integrative computational epigenomics to build data-driven gene regulation hypotheses.GigaScience · 2020Article
- The Road Not Taken with Pyrrole-Imidazole Polyamides: Off-Target Effects and Genomic Binding.Biomolecules · 2020Review
- AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU.BMC bioinformatics · 2019Article
- Article
- Prediction of LncRNA Subcellular Localization with Deep Learning from Sequence Features.Scientific reports · 2018Article
- Article
- A neural network based model effectively predicts enhancers from clinical ATAC-seq samples.Scientific reports · 2018Article
- Sequence based prediction of enhancer regions from DNA random walk.Scientific reports · 2018Article
- Deep learning in pharmacogenomics: from gene regulation to patient stratification.Pharmacogenomics · 2018Review
- Predicting double-strand DNA breaks using epigenome marks or DNA at kilobase resolution.Genome biology · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We present EP-DNN, a protocol for predicting enhancers based on chromatin features, in different cell types. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures in a representative human embryonic stem cell type (H1) and a differentiated lung cell type (IMR90). We train EP-DNN using p300 binding sites, as enhancers, and TSS and random non-DHS sites, as non-enhancers. We perform same-cell and cross-cell predictions to quantify the validation rate and compare against two state-of-the-art methods, DEEP-ENCODE and RFECS. We find that EP-DNN has superior accuracy with a validation rate of 91.6%, relative to 85.3% for DEEP-ENCODE and 85.5% for RFECS, for a given number of enhancer predictions and also scales better for a larger number of enhancer predictions. Moreover, our H1 → IMR90 predictions turn out to be more accurate than IMR90 → IMR90, potentially because H1 exhibits a richer signature set and our EP-DNN model is expressive enough to extract these subtleties. Our work shows how to leverage the full expressivity of deep learning models, using multiple hidden layers, while avoiding overfitting on the training data. We also lay the foundation for exploration of cross-cell enhancer predictions, potentially reducing the need for expensive experimentation.
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