ArticleFrontiers in plant science2020
6mA-RicePred: A Method for Identifying DNA
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 51 citations in OpenAlex.
- Cross-RNA transferable sequence representation learning for lncRNA m6A site detection via novel deep domain separation networks.Briefings in bioinformatics · 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
- 6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site.BioData mining · 2023Article
- H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA.Briefings in bioinformatics · 2023Article
- Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.PLoS computational biology · 2023Article
- Comparative evaluation and analysis of DNA N4-methylcytosine methylation sites using deep learning.Frontiers in genetics · 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
- 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
- BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches.Briefings in bioinformatics · 2022Article
- Biological Sequence Classification: A Review on Data and General Methods.Research (Washington, D.C.) · 2022Article
- Staem5: A novel computational approachfor accurate prediction of m5C site.Molecular therapy. Nucleic acids · 2021Article
- i6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites.Interdisciplinary sciences, computational life sciences · 2021Article
- A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome.Scientific reports · 2021Article
- Article
- i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation.Plant molecular biology · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationThe biological function of
resultsIn this paper, we propose a new computational method called 6mA-Pred to identify 6mA sites in the rice genome. 6mA-Pred employs a feature fusion method to combine advantageous features from other methods and thus obtain a new feature to identify 6mA sites. This method achieved an accuracy of 87.27% in the identification of 6mA sites with 10-fold cross-validation and achieved an accuracy of 85.6% in independent test sets.
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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.