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

Protein Networks02:26

Protein Networks

2.7K
2.7K
Protein Networks02:26

Protein Networks

4.4K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.4K
Neural Circuits01:25

Neural Circuits

2.5K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.5K

You might also read

Related Articles

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

Sort by
Same author

Neurologic Diagnoses Before and After Traumatic Brain Injury: A Retrospective Cohort Study of Older Veterans.

Neurology·2026
Same author

The surgical outcomes of modified Chen's U-suture technique compared with duct-to-mucosa anastomosis in laparoscopic pancreaticoduodenectomy: a multi-center cohort study.

Surgical endoscopy·2026
Same author

CollDTI: Dual-encoder collaborative learning for drug-target interaction prediction.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Hepatocellular Carcinoma Treatment with Immune Checkpoint Inhibitors: RECA and CRAFITY Scores Reveal Distinct Clinical Courses and Highlight the Role of Systemic Inflammation in Prognosis.

Biomedicines·2026
Same author

Enabling Drug-Drug Interaction Event Prediction with Multi-view-enhanced Chemical Structural Information.

Interdisciplinary sciences, computational life sciences·2026
Same author

HKD-CPI: high-order knowledge distillation enhanced inductive compound-protein interaction prediction.

Bioinformatics (Oxford, England)·2026

Related Experiment Video

Updated: Dec 22, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.6K

Application of deep learning methods in biological networks.

Shuting Jin, Xiangxiang Zeng, Feng Xia

    Briefings in Bioinformatics
    |May 5, 2020
    PubMed
    Summary

    Deep learning methods are crucial for analyzing complex biological networks and extracting valuable insights from large datasets. This approach enhances our understanding of biological systems, disease discovery, and drug development.

    Keywords:
    biological informationbiological networksbiomoleculedeep learningdeep neural networkgraph neural network

    More Related Videos

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
    09:34

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

    Published on: September 25, 2021

    4.4K
    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
    04:17

    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

    Published on: May 10, 2024

    1.3K

    Related Experiment Videos

    Last Updated: Dec 22, 2025

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    1.6K
    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
    09:34

    A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

    Published on: September 25, 2021

    4.4K
    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
    04:17

    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

    Published on: May 10, 2024

    1.3K

    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Machine Learning

    Background:

    • Biological networks are increasingly complex and large-scale.
    • Analyzing these networks is vital for understanding biological systems, diseases, and drug discovery.
    • Existing methods struggle with the complexity and heterogeneity of biological network data.

    Purpose of the Study:

    • To review deep learning models for biological network data analysis.
    • To summarize the applications of deep learning in biological networks.
    • To discuss future prospects in this field.

    Main Methods:

    • Introduction of network data deep learning models.
    • Summarization of deep learning applications on biological networks.
    • Discussion of future development prospects.

    Main Results:

    • Deep learning excels at extracting abstract features from large datasets.
    • Deep learning algorithms can process complex, heterogeneous graph data structures.
    • Deep learning is increasingly applied to mine information from biological networks.

    Conclusions:

    • Deep learning offers powerful tools for analyzing biological networks.
    • This technology aids in understanding biological systems and discovering therapeutic drugs.
    • The field holds significant promise for future advancements in bioinformatics.