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

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
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
Classification of Illness01:17

Classification of Illness

8.4K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.4K

You might also read

Related Articles

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

Sort by
Same author

Brusatol suppresses meningioma progression via targeting HMGCR to restrict the cholesterol biosynthesis and inhibit PI3K/AKT signaling pathway.

Frontiers in pharmacology·2026
Same author

MAGEC2 promotes tumorigenesis in multiple myeloma through USP16-mediated deubiquitination and stabilization of c-Myc.

Experimental hematology & oncology·2026
Same author

Oxymatrine alleviates cerebral ischemia-reperfusion injury by inhibiting microglia ferroptosis via NRF2 pathway activation.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

LuxS facilitates environmental adaptability and competition capability of avian pathogenic Escherichia coli.

Poultry science·2026
Same author

KRT18 promotes the high-grade meningioma proliferation by interacting with LDHA to activate glycolysis and the PI3K/AKT signaling pathway.

Cancer letters·2026
Same author

Targeting AVEN Liquid-Liquid Phase Separation in Colorectal Cancer: Insights From a Raddeanin A-Based Chemical Probe.

Basic & clinical pharmacology & toxicology·2026

Related Experiment Video

Updated: Jan 1, 2026

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 network embedding enabling accurate disease association predictions.

Yun Xiong1,2, Mengjie Guo1,2, Lu Ruan1,2

  • 1Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China.

BMC Medical Genomics
|December 24, 2019
PubMed
Summary

This study introduces a novel network embedding method to analyze complex biological data, improving the prediction of gene-disease and miRNA-disease associations for biological discovery.

Keywords:
Disease association predictionHeterogeneous networkNetwork embedding

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.5K

Related Experiment Videos

Last Updated: Jan 1, 2026

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
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.5K

Area of Science:

  • Biomedical research
  • Systems biology
  • Bioinformatics

Background:

  • Increasing biological data (genomics, proteomics) necessitates advanced analysis methods.
  • Existing data analysis capabilities lag behind data generation, hindering complex disease research.
  • Biological networks can represent diverse data types, offering potential for uncovering hidden relationships.

Purpose of the Study:

  • To develop a method for analyzing heterogeneous biological networks.
  • To improve the prediction of associations between biological entities like genes, diseases, and miRNAs.
  • To aid in generating new hypotheses for biological investigation.

Main Methods:

  • Constructed a heterogeneous biological network using six public databases, integrating genes, diseases, and miRNAs.
  • Developed a novel heterogeneous network embedding model to map the network into a low-dimensional vector space.
  • Evaluated the model's effectiveness through gene-disease and miRNA-disease association predictions.

Main Results:

  • The proposed method demonstrated superior performance compared to existing state-of-the-art approaches.
  • Predicted gene-disease and miRNA-disease associations were validated against recent real-world datasets.
  • The method effectively preserves relationships within the heterogeneous network structure.

Conclusions:

  • A novel heterogeneous network embedding method leverages contextual information and network structures.
  • The method aids in identifying new hypotheses for biological research.
  • This approach enhances the systematic exploration of complex biological mechanisms and diseases.