ArticleScientific reports2016
PEDLA: predicting enhancers with a deep learning-based algorithmic framework.
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 44 papers.
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Who cites it
44 citing papers in PubMed.
- Genomic language model for predicting enhancers and their allele-specific activity in the human genome.Bioinformatics (Oxford, England) · 2026Article
- EnhancerMatcher: comparing cell-type-specific enhancer activity of DNA sequences using deep convolutional neural networks and explainable AI.NAR genomics and bioinformatics · 2025Article
- Pig-eRNAdb: a comprehensive enhancer and eRNA dataset of pigs.Scientific data · 2024Article
- Integrative approaches based on genomic techniques in the functional studies on enhancers.Briefings in bioinformatics · 2023Review
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Computational methods for identifying enhancer-promoter interactions.Quantitative biology (Beijing, China) · 2023Review
- A Novel Repetition Frequency-Based DNA Encoding Scheme to Predict Human and Mouse DNA Enhancers with Deep Learning.Biomimetics (Basel, Switzerland) · 2023Article
- Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning.mSystems · 2022Article
- Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques.BMC bioinformatics · 2022Article
- Accurate prediction of functional states of cis-regulatory modules reveals common epigenetic rules in humans and mice.BMC biology · 2022Article
- Big data and computational advancements for next generation of Microbial Biotechnology.Microbial biotechnology · 2022Article
- Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases.Frontiers in aging neuroscience · 2022Review
- Machine learning applications for therapeutic tasks with genomics data.Patterns (New York, N.Y.) · 2021Review
- Annotating the Insect Regulatory Genome.Insects · 2021Review
- SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models.BMC research notes · 2021Article
- Application of deep learning in genomics.Science China. Life sciences · 2020Review
- Long-range chromatin interactions in pathogenic gene expression control.Transcription · 2020Review
- Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data.Genome biology · 2020Article
- A comprehensive review of computational prediction of genome-wide features.Briefings in bioinformatics · 2020Article
- Deep learning-based model for predicting progression in patients with head and neck squamous cell carcinoma.Cancer biomarkers : section A of Disease markers · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcriptional enhancers are non-coding segments of DNA that play a central role in the spatiotemporal regulation of gene expression programs. However, systematically and precisely predicting enhancers remain a major challenge. Although existing methods have achieved some success in enhancer prediction, they still suffer from many issues. We developed a deep learning-based algorithmic framework named PEDLA (https://github.com/wenjiegroup/PEDLA), which can directly learn an enhancer predictor from massively heterogeneous data and generalize in ways that are mostly consistent across various cell types/tissues. We first trained PEDLA with 1,114-dimensional heterogeneous features in H1 cells, and demonstrated that PEDLA framework integrates diverse heterogeneous features and gives state-of-the-art performance relative to five existing methods for enhancer prediction. We further extended PEDLA to iteratively learn from 22 training cell types/tissues. Our results showed that PEDLA manifested superior performance consistency in both training and independent test sets. On average, PEDLA achieved 95.0% accuracy and a 96.8% geometric mean (GM) of sensitivity and specificity across 22 training cell types/tissues, as well as 95.7% accuracy and a 96.8% GM across 20 independent test cell types/tissues. Together, our work illustrates the power of harnessing state-of-the-art deep learning techniques to consistently identify regulatory elements at a genome-wide scale from massively heterogeneous data across diverse cell types/tissues.
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Registered trials
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