ArticleFrontiers in genetics2021
iEnhancer-EBLSTM: Identifying Enhancers and Strengths by Ensembles of Bidirectional Long Short-Term Memory.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Hi-Enhancer: a two-stage framework for prediction and localization of enhancers based on Blending-KAN and Stacking-Auto models.Bioinformatics (Oxford, England) · 2026Article
- Article
- 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
- 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
- iKcr-DRC: prediction of lysine crotonylation sites in proteins based on a novel attention module and DenseNet.Frontiers in genetics · 2025Article
- DeepEnhancerPPO: An Interpretable Deep Learning Approach for Enhancer Classification.International journal of molecular sciences · 2024Article
- DeepDualEnhancer: A Dual-Feature Input DNABert Based Deep Learning Method for Enhancer Recognition.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
- iPro2L-DG: Hybrid network based on improved densenet and global attention mechanism for identifying promoter sequences.Heliyon · 2024Article
- i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites.BMC genomics · 2024Article
- Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers.Briefings in bioinformatics · 2024Article
- 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
- iEnhancer-DCSV: Predicting enhancers and their strength based on DenseNet and improved convolutional block attention module.Frontiers in genetics · 2023Article
- Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework.PLoS computational biology · 2022Article
- iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework.BMC bioinformatics · 2022Article
- Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition.Biomolecules · 2022Article
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6 authors.
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Abstract
Enhancers are regulatory DNA sequences that could be bound by specific proteins named transcription factors (TFs). The interactions between enhancers and TFs regulate specific genes by increasing the target gene expression. Therefore, enhancer identification and classification have been a critical issue in the enhancer field. Unfortunately, so far there has been a lack of suitable methods to identify enhancers. Previous research has mainly focused on the features of the enhancer's function and interactions, which ignores the sequence information. As we know, the recurrent neural network (RNN) and long short-term memory (LSTM) models are currently the most common methods for processing time series data. LSTM is more suitable than RNN to address the DNA sequence. In this paper, we take the advantages of LSTM to build a method named iEnhancer-EBLSTM to identify enhancers. iEnhancer-ensembles of bidirectional LSTM (EBLSTM) consists of two steps. In the first step, we extract subsequences by sliding a 3-mer window along the DNA sequence as features. Second, EBLSTM model is used to identify enhancers from the candidate input sequences. We use the dataset from the study of Quang H et al. as the benchmarks. The experimental results from the datasets demonstrate the efficiency of our proposed model.
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