ArticleBriefings in bioinformatics2021
Leveraging the attention mechanism to improve the identification of DNA N6-methyladenine sites.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk SNP in a transcript of RP11-638I2.4 increases lncRNA-YY1 interaction and pancreatic cancer susceptibility.Archives of toxicology · 2023Pooled it
- Small Vesicles Big Impact: Exosomes in the Landscape of Women's Health.Pharmaceutical research · 2026Review
- Deep learning-based identification of N6-methyladenine sites via hybrid feature fusion and SHAP-driven feature selection.Scientific reports · 2026Article
- [Prediction of protein Kbhb sites based on learnable feature embedding].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Molecular Indicator for Distinguishing Multi-drug-Resistant Tuberculosis from Drug Sensitivity Tuberculosis and Potential Medications for Treatment.Molecular biotechnology · 2025Article
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- A 4D tensor-enhanced multi-dimensional convolutional neural network for accurate prediction of protein-ligand binding affinity.Molecular diversity · 2025Article
- Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma.Interdisciplinary sciences, computational life sciences · 2025Article
- NRGCNMDA: Microbe-Drug Association Prediction Based on Residual Graph Convolutional Networks and Conditional Random Fields.Interdisciplinary sciences, computational life sciences · 2025Article
- Antibiotic SERS spectral analysis based on data augmentation and attention mechanism strategy.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2025Article
- Integrating BERT pre-training with graph common neighbours for predicting ceRNA interactions.Frontiers in genetics · 2025Article
- FSFT6mA: a feature-synthesis fine-tuning framework for DNA 6mA site prediction.Frontiers in genetics · 2025Article
- iResNetDM: An interpretable deep learning approach for four types of DNA methylation modification prediction.Computational and structural biotechnology journal · 2024Article
- ResisenseNet hybrid neural network model for predicting drug sensitivity and repurposing in breast Cancer.Scientific reports · 2024Article
- Application progress of deep generative models in de novo drug design.Molecular diversity · 2024Review
- Protein S-Nitrosylation: A Chemical Modification with Ubiquitous Biological Activities.The protein journal · 2024Review
- Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.Briefings in bioinformatics · 2024Review
- iDNA-ITLM: An interpretable and transferable learning model for identifying DNA methylation.PloS one · 2024Article
- iDNA-OpenPrompt: OpenPrompt learning model for identifying DNA methylation.Frontiers in genetics · 2024Article
- Analysis of whole transcriptome reveals the immune response to porcine reproductive and respiratory syndrome virus infection and tylvalosin tartrate treatment in the porcine alveolar macrophages.Frontiers in immunology · 2024Article
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7 authors.
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Abstract
DNA N6-methyladenine is an important type of DNA modification that plays important roles in multiple biological processes. Despite the recent progress in developing DNA 6mA site prediction methods, several challenges remain to be addressed. For example, although the hand-crafted features are interpretable, they contain redundant information that may bias the model training and have a negative impact on the trained model. Furthermore, although deep learning (DL)-based models can perform feature extraction and classification automatically, they lack the interpretability of the crucial features learned by those models. As such, considerable research efforts have been focused on achieving the trade-off between the interpretability and straightforwardness of DL neural networks. In this study, we develop two new DL-based models for improving the prediction of N6-methyladenine sites, termed LA6mA and AL6mA, which use bidirectional long short-term memory to respectively capture the long-range information and self-attention mechanism to extract the key position information from DNA sequences. The performance of the two proposed methods is benchmarked and evaluated on the two model organisms Arabidopsis thaliana and Drosophila melanogaster. On the two benchmark datasets, LA6mA achieves an area under the receiver operating characteristic curve (AUROC) value of 0.962 and 0.966, whereas AL6mA achieves an AUROC value of 0.945 and 0.941, respectively. Moreover, an in-depth analysis of the attention matrix is conducted to interpret the important information, which is hidden in the sequence and relevant for 6mA site prediction. The two novel pipelines developed for DNA 6mA site prediction in this work will facilitate a better understanding of the underlying principle of DL-based DNA methylation site prediction and its future applications.
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