ArticleBMC genomics2019
iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Genomic language model for predicting enhancers and their allele-specific activity in the human genome.Bioinformatics (Oxford, England) · 2026Article
- DcSE: an improved densenet with enhanced attention fusion for super-enhancer prediction.BMC genomics · 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
- ERV3-MLT1 provides cis-regulatory elements for human placental functioning and are commonly dysregulated in human-specific preeclampsia.Genome biology · 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
- Enhancer reprogramming: critical roles in cancer and promising therapeutic strategies.Cell death discovery · 2025Review
- AttnW2V-Enhancer: Leveraging attention and Word2Vec for enhanced enhancer prediction.Computational and structural biotechnology journal · 2025Article
- Utilizing a deep learning model based on BERT for identifying enhancers and their strength.PloS one · 2025Article
- EPI-DynFusion: enhancer-promoter interaction prediction model based on sequence features and dynamic fusion mechanisms.Frontiers in genetics · 2025Article
- DeepEnhancerPPO: An Interpretable Deep Learning Approach for Enhancer Classification.International journal of molecular sciences · 2024Article
- 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
- i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites.BMC genomics · 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
- Enhancer Recognition: A Transformer Encoder-Based Method with WGAN-GP for Data Augmentation.International journal of molecular sciences · 2023Article
- DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction.BMC genomics · 2023Article
- 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
- Genomic benchmarks: a collection of datasets for genomic sequence classification.BMC genomic data · 2023Article
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6 authors.
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Abstract
backgroundEnhancers are non-coding DNA fragments which are crucial in gene regulation (e.g. transcription and translation). Having high locational variation and free scattering in 98% of non-encoding genomes, enhancer identification is, therefore, more complicated than other genetic factors. To address this biological issue, several in silico studies have been done to identify and classify enhancer sequences among a myriad of DNA sequences using computational advances. Although recent studies have come up with improved performance, shortfalls in these learning models still remain. To overcome limitations of existing learning models, we introduce iEnhancer-ECNN, an efficient prediction framework using one-hot encoding and k-mers for data transformation and ensembles of convolutional neural networks for model construction, to identify enhancers and classify their strength. The benchmark dataset from Liu et al.'s study was used to develop and evaluate the ensemble models. A comparative analysis between iEnhancer-ECNN and existing state-of-the-art methods was done to fairly assess the model performance.
resultsOur experimental results demonstrates that iEnhancer-ECNN has better performance compared to other state-of-the-art methods using the same dataset. The accuracy of the ensemble model for enhancer identification (layer 1) and enhancer classification (layer 2) are 0.769 and 0.678, respectively. Compared to other related studies, improvements in the Area Under the Receiver Operating Characteristic Curve (AUC), sensitivity, and Matthews's correlation coefficient (MCC) of our models are remarkable, especially for the model of layer 2 with about 11.0%, 46.5%, and 65.0%, respectively.
conclusionsiEnhancer-ECNN outperforms other previously proposed methods with significant improvement in most of the evaluation metrics. Strong growths in the MCC of both layers are highly meaningful in assuring the stability of our models.
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