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

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
Protein Networks02:26

Protein Networks

2.7K
2.7K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.2K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.2K

You might also read

Related Articles

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

Sort by
Same author

Enhanced IGFL1 translation in response to IL-1β is controlled by distinct 3'UTR elements.

PloS one·2026
Same author

Autoencoder/RandomForest-TabPFN for cross-cancer metabolomics: prostate and breast cancer diagnosis using paper spray and ion mobility-mass spectrometry techniques.

GigaScience·2026
Same author

A leader-repeat hairpin blocks extraneous CRISPR RNA production in diverse CRISPR-Cas13 systems.

The EMBO journal·2026
Same author

Functional outcome after vertical parasagittal hemispherotomy in pediatric hemimegalencephaly: Insights from a single case.

Surgical neurology international·2026
Same author

MolVE: An Open-Source Web Platform for Visualizing and Evaluating AI-Designed Molecules to Aid in Prioritization.

Journal of chemical information and modeling·2026
Same author

Comprehensive analysis of CRISPR array repeat mutations reveals subtype-specific patterns and links to spacer dynamics.

microLife·2026

Related Experiment Video

Updated: Dec 29, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.0K

Heterogeneous networks integration for disease-gene prioritization with node kernels.

Van Dinh Tran1, Alessandro Sperduti2, Rolf Backofen1,3

  • 1Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany.

Bioinformatics (Oxford, England)
|January 29, 2020
PubMed
Summary
This summary is machine-generated.

This study integrates diverse gene interaction data into a unified network, improving disease-gene association identification. A novel node kernel effectively prioritizes genes, achieving state-of-the-art results in identifying crucial genetic links for diseases.

More Related Videos

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

829
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

2.6K

Related Experiment Videos

Last Updated: Dec 29, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.0K
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

829
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

2.6K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Identifying disease-gene associations is crucial for human health research.
  • Gene/protein interaction data is often represented as networks for analysis.
  • Integrating heterogeneous gene interaction sources enhances the reliability of gene prioritization.

Purpose of the Study:

  • To develop a robust method for identifying disease-gene associations by integrating diverse biological network data.
  • To improve the accuracy and reliability of gene prioritization for disease association studies.

Main Methods:

  • A three-phase approach: merging interaction sources into a single network, partitioning the network based on edge density and type, and applying a novel node kernel for typed graphs.
  • Utilizing a novel node kernel to generate discriminative features for machine learning classifiers.
  • Employing linear regularized machine learning classifiers for gene prioritization.

Main Results:

  • Achieved state-of-the-art performance on 12 disease-gene association datasets.
  • Validated findings on a time-stamped benchmark dataset, identifying 42 new disease-gene associations.
  • Demonstrated the effectiveness of the node kernel in generating features for accurate gene prioritization.

Conclusions:

  • The proposed method effectively integrates heterogeneous gene interaction data for improved disease-gene association discovery.
  • The novel node kernel approach enhances the capability of machine learning models in identifying disease-gene relationships.
  • This work provides a valuable tool for advancing human health research through more reliable gene prioritization.