ArticleScientific reports2016
EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features.
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 43 papers.
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Who cites it
43 citing papers in PubMed.
- iEnhancer-Flow: Integrating Transformer-Based Sequence Learning with DNA Shape Insights for Robust Enhancer Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Genomic language model for predicting enhancers and their allele-specific activity in the human genome.Bioinformatics (Oxford, England) · 2026Article
- Hi-Enhancer: a two-stage framework for prediction and localization of enhancers based on Blending-KAN and Stacking-Auto models.Bioinformatics (Oxford, England) · 2026Article
- Discovering the complete enhancer map of human herpesviruses using a natural language processing model.Nature communications · 2025Article
- iEnhancer-GDM: A Deep Learning Framework Based on Generative Adversarial Network and Multi-head Attention Mechanism to Identify Enhancers and Their Strength.Interdisciplinary sciences, computational life sciences · 2025Article
- Asymptotic Properties of Matthews Correlation Coefficient.Statistics in medicine · 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
- Utilizing a deep learning model based on BERT for identifying enhancers and their strength.PloS one · 2025Article
- AttnW2V-Enhancer: Leveraging attention and Word2Vec for enhanced enhancer prediction.Computational and structural biotechnology journal · 2025Article
- DeepEnhancerPPO: An Interpretable Deep Learning Approach for Enhancer Classification.International journal of molecular sciences · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- CapsEnhancer: An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network.Journal of chemical information and modeling · 2024Article
- A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding.iScience · 2024Article
- PlantNh-Kcr: a deep learning model for predicting non-histone crotonylation sites in plants.Plant methods · 2024Article
- StructuralDPPIV: a novel deep learning model based on atom structure for predicting dipeptidyl peptidase-IV inhibitory peptides.Bioinformatics (Oxford, England) · 2024Article
- ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction.Briefings in bioinformatics · 2024Article
- Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers.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
- Computational methods for identifying enhancer-promoter interactions.Quantitative biology (Beijing, China) · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Enhancers are cis elements that play an important role in regulating gene expression by enhancing it. Recent study of modifications revealed that enhancers are a large group of functional elements with many different subgroups, which have different biological activities and regulatory effects on target genes. As powerful auxiliary tools, several computational methods have been proposed to distinguish enhancers from other regulatory elements, but only one method has been considered to clustering them into subgroups. In this study, we developed a predictor (called EnhancerPred) to distinguish between enhancers and nonenhancers and to determine enhancers' strength. A two-step wrapper-based feature selection method was applied in high dimension feature vector from bi-profile Bayes and pseudo-nucleotide composition. Finally, the combination of 104 features from bi-profile Bayes, 1 feature from nucleotide composition and 9 features from pseudo-nucleotide composition yielded the best performance for identifying enhancers and nonenhancers, with overall Acc of 77.39%. The combination of 89 features from bi-profile Bayes and 10 features from pseudo-nucleotide composition yielded the best performance for identifying strong and weak enhancers, with overall Acc of 68.19%. The process and steps of feature optimization illustrated that it is necessary to construct a particular model for identifying strong enhancers and weak enhancers.
Identifiers
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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.