ArticleFrontiers in bioengineering and biotechnology2020
Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- MOSAIC enables selective transfer and fairness-aware generalization in cross-species DNA methylation prediction.Nucleic acids research · 2026Article
- EnDeep4mC predicts DNAGenome research · 2026Article
- 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
- iResNetDM: An interpretable deep learning approach for four types of DNA methylation modification prediction.Computational and structural biotechnology journal · 2024Article
- Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition.Cancers · 2024Review
- 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
- PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks.Animals : an open access journal from MDPI · 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
- Systematic Analysis and Accurate Identification of DNA N4-Methylcytosine Sites by Deep Learning.Frontiers in microbiology · 2022Article
- Biological Sequence Classification: A Review on Data and General Methods.Research (Washington, D.C.) · 2022Article
- Identifying DNA N4-methylcytosine sites in the rosaceae genome with a deep learning model relying on distributed feature representation.Computational and structural biotechnology journal · 2021Article
- DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species.Computational and structural biotechnology journal · 2021Article
- 4mCPred-MTL: Accurate Identification of DNA 4mC Sites in Multiple Species Using Multi-Task Deep Learning Based on Multi-Head Attention Mechanism.Frontiers in cell and developmental biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA N4-methylcytosine modification (4mC) plays an essential role in a variety of biological processes. Therefore, accurate identification the 4mC distribution in genome-scale is important for systematically understanding its biological functions. In this study, we present Deep4mcPred, a multi-layer deep learning based predictive model to identify DNA N4-methylcytosine modifications. In this predictor, we for the first time integrate residual network and recurrent neural network to build a multi-layer deep learning predictive system. As compared to existing predictors using traditional machine learning, our proposed method has two advantages. First, our deep learning framework does not need to specify the features when training the predictive model. It can automatically learn the high-level features and capture the characteristic specificity of 4mC sites, benefiting to distinguish true 4mC sites from non-4mC sites. On the other hand, our deep learning method outperforms the traditional machine learning predictors in performance by benchmarking comparison, demonstrating that the proposed Deep4mcPred is more effective in the DNA 4mC site prediction. Moreover, via experimental comparison, we found that attention mechanism introduced into the deep learning framework is useful to capture the critical features. Additionally, we develop a webserver implementing the proposed method for the academic use of research community, which is now available at http://server.malab.cn/Deep4mcPred.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.