ArticleBMC bioinformatics2022
Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 29 citations in OpenAlex.
- EnDeep4mC predicts DNAGenome research · 2026Article
- DNA Methylation Recognition Using Hybrid Deep Learning with Dual Nucleotide Visualization Fusion Feature Encoding.Interdisciplinary sciences, computational life sciences · 2025Article
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- iDNA-ITLM: An interpretable and transferable learning model for identifying DNA methylation.PloS one · 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
- Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.PLoS computational biology · 2023Article
- Identifying potential drug-target interactions based on ensemble deep learning.Frontiers in aging neuroscience · 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
- MuLan-Methyl-multiple transformer-based language models for accurate DNA methylation prediction.GigaScience · 2022Article
- Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework.iScience · 2022Article
- Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization.Frontiers in genetics · 2022Article
- Editorial: Machine learning-based methods for RNA data analysis-Volume II.Frontiers in genetics · 2022Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundDNA N4-methylcytosine is part of the restrictive modification system, which works by regulating some biological processes, for example, the initiation of DNA replication, mismatch repair and inactivation of transposon. However, using experimental methods to detect 4mC sites is time-consuming and expensive. Besides, considering the huge differences in the number of 4mC samples among different species, it is challenging to achieve a robust multi-species 4mC site prediction performance. Hence, it is of great significance to develop effective computational tools to identify 4mC sites.
resultsThis work proposes a flexible deep learning-based framework to predict 4mC sites, called Hyb4mC. Hyb4mC adopts the DNA2vec method for sequence embedding, which captures more efficient and comprehensive information compared with the sequence-based feature method. Then, two different subnets are used for further analysis: Hyb_Caps and Hyb_Conv. Hyb_Caps is composed of a capsule neural network and can generalize from fewer samples. Hyb_Conv combines the attention mechanism with a text convolutional neural network for further feature learning.
conclusionsExtensive benchmark tests have shown that Hyb4mC can significantly enhance the performance of predicting 4mC sites compared with the recently proposed 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.