ArticleFrontiers in genetics2019
iDNA6mA-Rice: A Computational Tool for Detecting N6-Methyladenine Sites in Rice.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
- Identification of DNA N6-methyladenine modifications in the rice genome with a fine-tuned large language model.Frontiers in plant science · 2025Article
- FSFT6mA: a feature-synthesis fine-tuning framework for DNA 6mA site prediction.Frontiers in genetics · 2025Article
- DeepRice6mA: A convolutional neural network approach for 6mA site prediction in the rice Genome.PloS one · 2025Article
- Article
- 6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site.BioData mining · 2023Article
- MethSemble-6mA: an ensemble-based 6mA prediction server and its application on promoter region of LBD gene family in Poaceae.Frontiers in plant science · 2023Article
- DRSN4mCPred: accurately predicting sites of DNA N4-methylcytosine using deep residual shrinkage network for diagnosis and treatment of gastrointestinal cancer in the precision medicine era.Frontiers in medicine · 2023Article
- 4acCPred: Weakly supervised prediction ofMolecular therapy. Nucleic acids · 2022Article
- Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning.International journal of molecular sciences · 2022Review
- Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy.Molecular therapy : the journal of the American Society of Gene Therapy · 2022Article
- ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks.International journal of molecular sciences · 2022Article
- Machine learning: its challenges and opportunities in plant system biology.Applied microbiology and biotechnology · 2022Review
- BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches.Briefings in bioinformatics · 2022Article
- i6mA-Vote: Cross-Species Identification of DNA N6-Methyladenine Sites in Plant Genomes Based on Ensemble Learning With Voting.Frontiers in plant science · 2022Article
- Leveraging the attention mechanism to improve the identification of DNA N6-methyladenine sites.Briefings in bioinformatics · 2021Article
- A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome.Scientific reports · 2021Article
- 4mCPred-CNN-Prediction of DNA N4-Methylcytosine in the Mouse Genome Using a Convolutional Neural Network.Genes · 2021Article
- Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species.PLoS computational biology · 2021Article
- Harnessing Current Knowledge of DNA N6-Methyladenosine From Model Plants for Non-model Crops.Frontiers in genetics · 2021Review
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Corrections and comments
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
DNA N6-methyladenine (6mA) is a dominant DNA modification form and involved in many biological functions. The accurate genome-wide identification of 6mA sites may increase understanding of its biological functions. Experimental methods for 6mA detection in eukaryotes genome are laborious and expensive. Therefore, it is necessary to develop computational methods to identify 6mA sites on a genomic scale, especially for plant genomes. Based on this consideration, the study aims to develop a machine learning-based method of predicting 6mA sites in the rice genome. We initially used mono-nucleotide binary encoding to formulate positive and negative samples. Subsequently, the machine learning algorithm named Random Forest was utilized to perform the classification for identifying 6mA sites. Our proposed method could produce an area under the receiver operating characteristic curve of 0.964 with an overall accuracy of 0.917, as indicated by the fivefold cross-validation test. Furthermore, an independent dataset was established to assess the generalization ability of our method. Finally, an area under the receiver operating characteristic curve of 0.981 was obtained, suggesting that the proposed method had good performance of predicting 6mA sites in the rice genome. For the convenience of retrieving 6mA sites, on the basis of the computational method, we built a freely accessible web server named iDNA6mA-Rice at http://lin-group.cn/server/iDNA6mA-Rice.
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