Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Catalytic enantioselective access to N(III)-stereogenic aziridines <i>via</i> chiral brønsted acid-catalyzed N-Cl bond formation.

Chemical communications (Cambridge, England)·2026

Same author

A Multi-Modal Contrastive Learning Framework for Cyclic Peptide Permeability Prediction.

IEEE transactions on computational biology and bioinformatics·2025

Same author

Control Over S(VI) Stereogenicity for the Asymmetric Synthesis of Sulfonimidoyl Derivatives by Isothiourea-Catalyzed Covalent Activation of Sulfur(VI) Atoms.

Angewandte Chemie (International ed. in English)·2025

Same author

Asymmetric catalytic reductive transformation of S(VI) to S(IV) for modular access to S(IV)-stereogenic sulfilimine derivatives.

Science advances·2025

Same author

Heteroarylation of Sulfenamides for Modular Synthesis of Antimicrobial Sulfilimines via Sulfinimidoyl Fluoride Intermediates.

Organic letters·2025

Same author

Asymmetric Synthesis of S(IV)-Stereogenic Sulfinimidate Esters by Sulfinamide Activation.

Angewandte Chemie (International ed. in English)·2025

Search research articles

Related Experiment Video

Updated: Dec 22, 2025

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

Published on: September 7, 2017

Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications.

Rao Zeng¹, Minghong Liao¹

¹Department of Software Engineering, School of Informatics, Xiamen University, Xiamen, China.

Frontiers in Bioengineering and Biotechnology

|May 7, 2020

Summary

This summary is machine-generated.

Deep4mcPred accurately identifies DNA N4-methylcytosine (4mC) modifications using deep learning. This novel approach outperforms traditional methods, aiding in understanding 4mC

Keywords:

DNA N4-methylcytosine deep learning feature representation site prediction webserver

More Related Videos

The Detection of 5-Hydroxymethylcytosine in Neural Stem Cells and Brains of Mice

The Detection of 5-Hydroxymethylcytosine in Neural Stem Cells and Brains of Mice

Published on: September 19, 2019

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Published on: June 16, 2017

Related Experiment Videos

Last Updated: Dec 22, 2025

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

Published on: September 7, 2017

The Detection of 5-Hydroxymethylcytosine in Neural Stem Cells and Brains of Mice

The Detection of 5-Hydroxymethylcytosine in Neural Stem Cells and Brains of Mice

Published on: September 19, 2019

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Published on: June 16, 2017

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

DNA N4-methylcytosine (4mC) modification is crucial for various biological processes.
Accurate identification of 4mC distribution is essential for understanding its genomic functions.

Purpose of the Study:

To develop a novel deep learning-based predictive model, Deep4mcPred, for identifying DNA 4mC modifications.
To improve the accuracy and efficiency of 4mC site prediction compared to existing methods.

Main Methods:

Integration of residual network and recurrent neural network for a multi-layer deep learning system.
Automatic feature learning to capture specific characteristics of 4mC sites.
Incorporation of an attention mechanism to identify critical features.

Main Results:

Deep4mcPred demonstrates superior performance over traditional machine learning predictors.
The deep learning framework effectively distinguishes true 4mC sites from non-4mC sites without manual feature engineering.
The attention mechanism proved beneficial in capturing key features for prediction.

Conclusions:

Deep4mcPred is a highly effective tool for DNA 4mC site prediction.
The developed webserver provides a valuable resource for the research community.