Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

RNA-seq03:21

RNA-seq

10.2K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
10.2K
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

10.0K
Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
10.0K

You might also read

Related Articles

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

Sort by
Same author

Interpretable Diagnosis for Whole-Slide Melanoma Histology Images Using Convolutional Neural Network.

Journal of healthcare engineering·2021
Same author

Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study.

Journal of healthcare engineering·2021
Same author

Survivin, the promising target in hepatocellular carcinoma gene therapy.

Cancer biology & therapy·2008
Same author

Curcumin protects dopaminergic neuron against LPS induced neurotoxicity in primary rat neuron/glia culture.

Neurochemical research·2008
Same author

Cellular mechanisms of reduced sarcoplasmic reticulum Ca2+ content in L-thyroxin induced rat ventricular hypertrophy.

Acta pharmacologica Sinica·2008
Same author

Promoting the formation and stabilization of G-quadruplex by dinuclear RuII complex Ru2(obip)L4.

Inorganic chemistry·2008

Related Experiment Video

Updated: Aug 8, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K

Gene Regulatory Network Inference Using Convolutional Neural Networks from scRNA-seq Data.

Guo Mao1, Zhengbin Pang1, Ke Zuo1

  • 1Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|March 6, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces CNNSE, a novel deep learning method for reconstructing gene regulatory networks from single-cell RNA sequencing data. CNNSE enhances accuracy by analyzing 2D co-expression matrices, overcoming noise and dropout challenges in gene expression analysis.

Keywords:
2D co-expression matrixconvolutional neural networkscRNA-seq datathe atrous spatial pyramid pooling (ASPP) module and the squeeze-and-excitation (SE) module

More Related Videos

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing
06:38

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Published on: October 12, 2018

18.9K
Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
11:26

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

13.9K

Related Experiment Videos

Last Updated: Aug 8, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K
Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing
06:38

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Published on: October 12, 2018

18.9K
Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
11:26

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

13.9K

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) offers high-resolution gene expression data.
  • Reconstructing gene regulatory networks (GRNs) is crucial for understanding cellular mechanisms.
  • Traditional GRN inference methods struggle with scRNA-seq data noise and dropout.

Purpose of the Study:

  • To develop a novel method for accurate gene regulatory network reconstruction using scRNA-seq data.
  • To address the challenges posed by noise and dropout in scRNA-seq data.
  • To improve the precision and stability of gene regulatory network inference.

Main Methods:

  • A supervised convolutional neural network (CNNSE) model was developed.
  • CNNSE utilizes 2D co-expression matrices of gene pairs to extract gene expression information.
  • The method avoids extreme point interference and captures detailed semantic information.

Main Results:

  • CNNSE achieved high accuracy on simulated data (ACC: 0.712, F1: 0.724).
  • The model demonstrated superior stability and accuracy compared to existing algorithms on real scRNA-seq datasets.
  • Improved precision in identifying gene-gene regulatory relationships was observed.

Conclusions:

  • CNNSE offers a robust and accurate approach for gene regulatory network reconstruction from scRNA-seq data.
  • The method effectively mitigates the impact of data noise and dropout.
  • CNNSE provides a valuable tool for advancing systems biology and understanding gene regulation.