ArticleBMC genomics2023
iEnhancer-DCSA: identifying enhancers via dual-scale convolution and spatial attention.
Article in BMC genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 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
- 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
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundDue to the dynamic nature of enhancers, identifying enhancers and their strength are major bioinformatics challenges. With the development of deep learning, several models have facilitated enhancers detection in recent years. However, existing studies either neglect different length motifs information or treat the features at all spatial locations equally. How to effectively use multi-scale motifs information while ignoring irrelevant information is a question worthy of serious consideration. In this paper, we propose an accurate and stable predictor iEnhancer-DCSA, mainly composed of dual-scale fusion and spatial attention, automatically extracting features of different length motifs and selectively focusing on the important features.
resultsOur experimental results demonstrate that iEnhancer-DCSA is remarkably superior to existing state-of-the-art methods on the test dataset. Especially, the accuracy and MCC of enhancer identification are improved by 3.45% and 9.41%, respectively. Meanwhile, the accuracy and MCC of enhancer classification are improved by 7.65% and 18.1%, respectively. Furthermore, we conduct ablation studies to demonstrate the effectiveness of dual-scale fusion and spatial attention.
conclusionsiEnhancer-DCSA will be a valuable computational tool in identifying and classifying enhancers, especially for those not included in the training dataset.
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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 Socratic graph.