ArticleScientific reports2021
A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- MeDiCNet: Integrating Multi-scale Dynamic Convolution and Enhanced Position-Aware Transformer for DNA Methylation Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Integrated epigenomic and transcriptional profiling reveals genotype-specific adaptive reprogramming to drought stress in Brassica napus.BMC plant biology · 2026Article
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 2025Review
- DeepRice6mA: A convolutional neural network approach for 6mA site prediction in the rice Genome.PloS one · 2025Article
- Benchmarking recent computational tools for DNA-binding protein identification.Briefings in bioinformatics · 2024Article
- The novel roles of RNA m6A modification in regulating the development, infection, and oxidative DNA damage repair of Phytophthora sojae.PLoS pathogens · 2024Article
- Epigenetic targeting of autophagy for cancer: DNA and RNA methylation.Frontiers in oncology · 2023Review
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4 authors.
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Abstract
DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money. Our study develops a convolutional neural network (CNN) based tool i6mA-CNN capable of identifying 6mA sites in the rice genome. Our model coordinates among multiple types of features such as PseAAC (Pseudo Amino Acid Composition) inspired customized feature vector, multiple one hot representations and dinucleotide physicochemical properties. It achieves auROC (area under Receiver Operating Characteristic curve) score of 0.98 with an overall accuracy of 93.97% using fivefold cross validation on benchmark dataset. Finally, we evaluate our model on three other plant genome 6mA site identification test datasets. Results suggest that our proposed tool is able to generalize its ability of 6mA site identification on plant genomes irrespective of plant species. An algorithm for potential motif extraction and a feature importance analysis procedure are two by products of this research. Web tool for this research can be found at: https://cutt.ly/dgp3QTR .
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