ArticleBMC bioinformatics2017
A new method for enhancer prediction based on deep belief network.
Article in BMC bioinformatics, 2017. 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.
- RWDisEnh+: Enhancing disease-enhancer association prediction through multiplex-heterogeneous networks.PloS one · 2026Article
- AttnW2V-Enhancer: Leveraging attention and Word2Vec for enhanced enhancer prediction.Computational and structural biotechnology journal · 2025Article
- ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction.Briefings in bioinformatics · 2024Article
- PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks.Animals : an open access journal from MDPI · 2023Article
- iEnhancer-DCSA: identifying enhancers via dual-scale convolution and spatial attention.BMC genomics · 2023Article
- A Novel Repetition Frequency-Based DNA Encoding Scheme to Predict Human and Mouse DNA Enhancers with Deep Learning.Biomimetics (Basel, Switzerland) · 2023Article
- Decoding enhancer complexity with machine learning and high-throughput discovery.Genome biology · 2023Review
- iEnhancer-DCSV: Predicting enhancers and their strength based on DenseNet and improved convolutional block attention module.Frontiers in genetics · 2023Article
- An Efficient Lightweight Hybrid Model with Attention Mechanism for Enhancer Sequence Recognition.Biomolecules · 2022Article
- A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns.Scientific reports · 2022Article
- A Novel Position-Specific Encoding Algorithm (SeqPose) of Nucleotide Sequences and Its Application for Detecting Enhancers.International journal of molecular sciences · 2021Article
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
- Article
- Learning the Regulatory Code of Gene Expression.Frontiers in molecular biosciences · 2021Review
- Integrative prediction of gene expression with chromatin accessibility and conformation data.Epigenetics & chromatin · 2020Article
- iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks.BMC genomics · 2019Article
- Article
- Article
- Beyond the Exome: The Non-coding Genome and Enhancers in Neurodevelopmental Disorders and Malformations of Cortical Development.Frontiers in cellular neuroscience · 2019Article
- Sequence based prediction of enhancer regions from DNA random walk.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStudies have shown that enhancers are significant regulatory elements to play crucial roles in gene expression regulation. Since enhancers are unrelated to the orientation and distance to their target genes, it is a challenging mission for scholars and researchers to accurately predicting distal enhancers. In the past years, with the high-throughout ChiP-seq technologies development, several computational techniques emerge to predict enhancers using epigenetic or genomic features. Nevertheless, the inconsistency of computational models across different cell-lines and the unsatisfactory prediction performance call for further research in this area.
resultsHere, we propose a new Deep Belief Network (DBN) based computational method for enhancer prediction, which is called EnhancerDBN. This method combines diverse features, composed of DNA sequence compositional features, DNA methylation and histone modifications. Our computational results indicate that 1) EnhancerDBN outperforms 13 existing methods in prediction, and 2) GC content and DNA methylation can serve as relevant features for enhancer prediction.
conclusionDeep learning is effective in boosting the performance of enhancer prediction.
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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.