ArticleBriefings in bioinformatics2021
DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine 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 51 papers.
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51 citing papers in PubMed, 129 citations in OpenAlex.
- EnDeep4mC predicts DNAGenome research · 2026Article
- Mamba6mA: a Mamba-based DNA N6-methyladenine site prediction model.Bioinformatics (Oxford, England) · 2026Article
- A New Unsupervised Machine Learning Method for Metabarcoding Analysis Based on Selected Mock Community Data.Computational and structural biotechnology journal · 2026Article
- DNA Methylation Recognition Using Hybrid Deep Learning with Dual Nucleotide Visualization Fusion Feature Encoding.Interdisciplinary sciences, computational life sciences · 2025Article
- iDNA-DAPHA: a generic framework for methylation prediction via domain-adaptive pretraining and hierarchical attention.Briefings in bioinformatics · 2025Article
- DMRU: generative deep learning to unravel condition-specific cytosine methylation in plants.Briefings in bioinformatics · 2025Article
- Single-Droplet Dual-Target Quantification of circRNA Biomarkers for Colorectal Cancer Screening.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- RiceSNP-ABST: a deep learning approach to identify abiotic stress-associated single nucleotide polymorphisms in rice.Briefings in bioinformatics · 2024Article
- A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications.Scientific reports · 2024Article
- Advancing mRNA subcellular localization prediction with graph neural network and RNA structure.Bioinformatics (Oxford, England) · 2024Article
- Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns.Scientific reports · 2024Article
- m6A-TCPred: a web server to predict tissue-conserved human mBMC bioinformatics · 2024Article
- iDNA-OpenPrompt: OpenPrompt learning model for identifying DNA methylation.Frontiers in genetics · 2024Article
- Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction.BMC genomics · 2023Article
- StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning.BMC genomics · 2023Article
- A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.Nucleic acids research · 2023Article
- DLC-ac4C: A Prediction Model for N4-acetylcytidine Sites in Human mRNA Based on DenseNet and Bidirectional LSTM Methods.Current genomics · 2023Article
- MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation.Theory in biosciences = Theorie in den Biowissenschaften · 2023Article
- Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
DNA N4-methylcytosine (4mC) is an important epigenetic modification that plays a vital role in regulating DNA replication and expression. However, it is challenging to detect 4mC sites through experimental methods, which are time-consuming and costly. Thus, computational tools that can identify 4mC sites would be very useful for understanding the mechanism of this important type of DNA modification. Several machine learning-based 4mC predictors have been proposed in the past 3 years, although their performance is unsatisfactory. Deep learning is a promising technique for the development of more accurate 4mC site predictions. In this work, we propose a deep learning-based approach, called DeepTorrent, for improved prediction of 4mC sites from DNA sequences. It combines four different feature encoding schemes to encode raw DNA sequences and employs multi-layer convolutional neural networks with an inception module integrated with bidirectional long short-term memory to effectively learn the higher-order feature representations. Dimension reduction and concatenated feature maps from the filters of different sizes are then applied to the inception module. In addition, an attention mechanism and transfer learning techniques are also employed to train the robust predictor. Extensive benchmarking experiments demonstrate that DeepTorrent significantly improves the performance of 4mC site prediction compared with several state-of-the-art methods.
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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 OpenQuestion graph.