iEnhancer-DCSA: identifying enhancers via dual-scale convolution and spatial attention

Wang, Wenjun; Wu, Qingyao; Li, Chunshan

doi:10.1186/s12864-023-09468-1

Cited by 4 publications

(2 citation statements)

References 36 publications

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“… Wang et al. 28 iEnhancer-GAN Building a CNN architecture by combining word embedding skip-gram and sequence generation adversarial networks Bao et al. 31 Enhancer-LSTMAtt After simple encoding of sequences, using Bi-LSTM and attention-based deep learning methods Huang et al.…”

Section: Resultsmentioning

confidence: 99%

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A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding

Hu,

Li,

et al. 2024

iScience

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Section: Resultsmentioning

confidence: 99%

“…They eventually developed the iEnhancer-DCSA model, a convolutional neural network employing dual-scale fusion. 28 Basith et al. utilized seven encodings, including DPCP and k-mer, and integrated five machine learning methods, such as RF, SVM, and XGB, to establish an enhancer prediction model.…”

Section: Introductionmentioning

confidence: 99%

A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding

Hu,

Li,

et al. 2024

iScience

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CapsEnhancer: An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network

Yao,

Xie,

Guan

et al. 2024

J. Chem. Inf. Model.

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Enhancers are a class of noncoding DNA, serving as crucial regulatory elements in governing gene expression by binding to transcription factors. The identification of enhancers holds paramount importance in the field of biology. However, traditional experimental methods for enhancer identification demand substantial human and material resources. Consequently, there is a growing interest in employing computational methods for enhancer prediction. In this study, we propose a two-stage framework based on deep learning, termed CapsEnhancer, for the identification of enhancers and their strengths. CapsEnhancer utilizes chaos game representation to encode DNA sequences into unique images and employs a capsule network to extract local and global features from sequence "images". Experimental results demonstrate that CapsEnhancer achieves state-of-the-art performance in both stages. In the first and second stages, the accuracy surpasses the previous best methods by 8 and 3.5%, reaching accuracies of 94.5 and 95%, respectively. Notably, this study represents the pioneering application of computer vision methods to enhancer identification tasks. Our work not only contributes novel insights to enhancer identification but also provides a fresh perspective for other biological sequence analysis tasks.

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DeepEnhancerPPO: An Interpretable Deep Learning Approach for Enhancer Classification

Mu,

Chen,

Shi

et al. 2024

Preprint

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Enhancers are short genomic segments located in non-coding regions in a genome that help to increase the expressions of the target genes. Despite their significance in transcription regulation, effective methods for classifying enhancer categories and regulatory strengths remain limited. To address the issue, we propose a novel end-to-end deep learning architecture named DeepEnhancerPPO. The model integrates ResNet and Transformer modules to extract local, hierarchical, and long-range contextual features. Following feature fusion, we employ the proximal policy optimization (PPO), a reinforcement learning technique, to reduce the dimensionality of the fused features, retaining the most relevant ones for downstream classification. We evaluate the performance of DeepEnhancerPPO from multiple perspectives, including ablation analysis, independent tests, and interpretability of classification results. Each of these modules contributes positively to the model's performance, with ResNet and PPO being the top contributors. Overall, DeepEnhancerPPO exhibits superb performance on independent datasets compared to other models, outperforming the second-best model by 6.7% in accuracy for enhancer category classification. The model also ranks within the top five classifiers out of 25 in enhancer strength classification without the need to re-optimize the hyperparameters, indicating that the DeepEnhancerPPO framework is highly robust for enhancer classification. Additionally, the inclusion of PPO enhances the interpretability of the classification results. The source code is openly accessible at https://github.com/Mxc666/DeepEnhancerPPO.git.

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iEnhancer-DCSA: identifying enhancers via dual-scale convolution and spatial attention

Cited by 4 publications

References 36 publications

A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding

A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding

CapsEnhancer: An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network

DeepEnhancerPPO: An Interpretable Deep Learning Approach for Enhancer Classification

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