ArticleComputational and mathematical methods in medicine2020
Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- RMNet: An RNA m6A Cross-species Methylation Detection Method for Nanopore Sequencing.Current drug targets · 2025Article
- Utilizing a deep learning model based on BERT for identifying enhancers and their strength.PloS one · 2025Article
- DeepEnhancerPPO: An Interpretable Deep Learning Approach for Enhancer Classification.International journal of molecular sciences · 2024Article
- EMDL-ac4C: identifying N4-acetylcytidine based on ensemble two-branch residual connection DenseNet and attention.Frontiers in genetics · 2023Article
- Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition.Biomolecules · 2022Article
- Analysis of the landscape of human enhancer sequences in biological databases.Computational and structural biotechnology journal · 2022Article
- Genomic Island Prediction via Chi-Square Test and Random Forest Algorithm.Computational and mathematical methods in medicine · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Enhancers are noncoding fragments in DNA sequences, which play an important role in gene transcription and translation. However, due to their high free scattering and positional variability, the identification and classification of enhancers have a higher level of complexity than those of coding genes. In order to solve this problem, many computer studies have been carried out in this field, but there are still some deficiencies in these prediction models. In this paper, we use various feature extraction strategies, dimension reduction technology, and a comprehensive application of machine model and recurrent neural network model to achieve an accurate prediction of enhancer identification and classification with the accuracy of was 76.7% and 84.9%, respectively. The model proposed in this paper is superior to the previous methods in performance index or feature dimension, which provides inspiration for the prediction of enhancers by computer technology in the future.
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Registered trials
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