ArticleFrontiers in plant science2022
i6mA-Vote: Cross-Species Identification of DNA N6-Methyladenine Sites in Plant Genomes Based on Ensemble Learning With Voting.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 29 citations in OpenAlex.
- Deep learning-based identification of N6-methyladenine sites via hybrid feature fusion and SHAP-driven feature selection.Scientific reports · 2026Article
- DMRU: generative deep learning to unravel condition-specific cytosine methylation in plants.Briefings in bioinformatics · 2025Article
- Article
- Identification of DNA N6-methyladenine modifications in the rice genome with a fine-tuned large language model.Frontiers in plant science · 2025Article
- A robust deep learning approach for identification of RNA 5-methyluridine sites.Scientific reports · 2024Article
- DeepPGD: A Deep Learning Model for DNA Methylation Prediction Using Temporal Convolution, BiLSTM, and Attention Mechanism.International journal of molecular sciences · 2024Article
- 6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site.BioData mining · 2023Article
- Recall DNA methylation levels at low coverage sites using a CNN model in WGBS.PLoS computational biology · 2023Article
- iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers.BMC bioinformatics · 2023Article
- Computational prediction of promotors inFrontiers in microbiology · 2023Article
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Authors and funding
7 authors at 5 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
DNA N6-Methyladenine (6mA) is a common epigenetic modification, which plays some significant roles in the growth and development of plants. It is crucial to identify 6mA sites for elucidating the functions of 6mA. In this article, a novel model named i6mA-vote is developed to predict 6mA sites of plants. Firstly, DNA sequences were coded into six feature vectors with diverse strategies based on density, physicochemical properties, and position of nucleotides, respectively. To find the best coding strategy, the feature vectors were compared on several machine learning classifiers. The results suggested that the position of nucleotides has a significant positive effect on 6mA sites identification. Thus, the dinucleotide one-hot strategy which can describe position characteristics of nucleotides well was employed to extract DNA features in our method. Secondly, DNA sequences of Rosaceae were divided into a training dataset and a test dataset randomly. Finally, i6mA-vote was constructed by combining five different base-classifiers under a majority voting strategy and trained on the Rosaceae training dataset. The i6mA-vote was evaluated on the task of predicting 6mA sites from the genome of the Rosaceae, Rice, and Arabidopsis separately. In Rosaceae, the performances of i6mA-vote were 0.955 on accuracy (ACC), 0.909 on Matthew correlation coefficients (MCC), 0.955 on sensitivity (SN), and 0.954 on specificity (SP). Those indicators, in the order of ACC, MCC, SN, SP, were 0.882, 0.774, 0.961, and 0.803 on Rice while they were 0.798, 0.617, 0.666, and 0.929 on Arabidopsis. According to the indicators, our method was effectiveness and better than other concerned methods. The results also illustrated that i6mA-vote does not only well in 6mA sites prediction of intraspecies but also interspecies plants. Moreover, it can be seen that the specificity is distinctly lower than the sensitivity in Rice while it is just the opposite in Arabidopsis. It may be resulted from sequence similarity among Rosaceae, Rice and Arabidopsis.
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