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-protein Interfaces02:04

Protein-protein Interfaces

14.4K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
14.4K
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

9.9K
Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
9.9K

You might also read

Related Articles

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

Sort by
Same author

A dynamic risk prediction framework for Alzheimer's disease and related dementias with interpretability.

NPJ digital medicine·2026
Same author

Clinical document metadata extraction: A scoping review.

Journal of biomedical informatics·2026
Same author

Context matching is not reasoning when performing generalized clinical evaluation of generative language models.

NPJ digital medicine·2025
Same author

A Self-Explainable Dynamic Risk Monitoring Framework for Predicting Alzheimer's Disease and Related Dementias.

medRxiv : the preprint server for health sciences·2025
Same author

Discovering signature disease trajectories in pancreatic cancer and soft-tissue sarcoma from longitudinal patient records.

Journal of biomedical informatics·2025
Same author

Advancing delirium detection through the Open Health Natural Language Processing Consortium and the Evolve to Next-Gen Accrual to Clinical Trials Network.

The journals of gerontology. Series A, Biological sciences and medical sciences·2025

Related Experiment Video

Updated: Jan 1, 2026

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

839

Drug-target prediction utilizing heterogeneous bio-linked network embeddings.

Nansu Zong1, Rachael Sze Nga Wong2, Yue Yu1

  • 1Department of Health Sciences Research, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA.

Briefings in Bioinformatics
|December 31, 2019
PubMed
Summary

This study benchmarks network-based drug-target prediction methods, identifying optimal strategies and subnetworks. The developed framework accurately predicts novel drug-target associations and reveals disease-network topology links.

Keywords:
biomedical knowledge networkdrug-target predictiongraph embedding

More Related Videos

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
13:18

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

Published on: March 3, 2023

1.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.2K

Related Experiment Videos

Last Updated: Jan 1, 2026

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

839
Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
13:18

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

Published on: March 3, 2023

1.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.2K

Area of Science:

  • Computational Biology
  • Network Science
  • Pharmacology

Background:

  • Drug-target prediction is crucial for pharmaceutical research.
  • Existing network-based methods require optimization for modularization and performance.
  • A comprehensive evaluation of different network structures and prediction algorithms is needed.

Purpose of the Study:

  • To benchmark various network permutations and machine learning methods for drug-target prediction.
  • To develop a modularized, high-performing network-based prediction framework.
  • To identify optimal strategies for association mining and prediction in biomedical networks.

Main Methods:

  • Benchmarking 32 subnetwork permutations from a heterogeneous biomedical network (12 repositories).
  • Evaluating combinations of classification, inference, and graph embedding methods.
  • Conducting six distinct experimental tasks from network and methodological perspectives.
  • Performing disease-specific prediction tasks for 20 diseases.

Main Results:

  • The proposed network-based framework outperformed existing methods.
  • Combinatorial network structures and methodologies significantly influence prediction accuracy.
  • Successfully predicted 75 novel drug-target associations, validated against DrugBank 5.1.0.
  • Linked network topology to biological explanations for 'Asthma', 'Hypertension', and 'Dementia' predictions.

Conclusions:

  • The study provides a robust, modularized network-based prediction framework.
  • The framework serves as a baseline for evaluating diverse data sources and machine learning algorithms.
  • Demonstrated the utility of network topology in understanding disease-specific drug-target relationships.