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

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

You might also read

Related Articles

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

Sort by
Same author

Clinical Application and Research Advances of PET Molecular Imaging in Immune Checkpoint Inhibitor-Associated Myocarditis.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology·2026
Same author

Overlapping and distinct fatty acid dysregulation in infertility and recurrent spontaneous abortion.

Frontiers in endocrinology·2026
Same author

Development and validation of a machine learning-based risk prediction model for cancer-related fatigue in ovarian cancer patients.

Frontiers in oncology·2026
Same author

FAIR in practice: minimum metadata schema for bioinformatics analytics by machines.

Journal of biomedical semantics·2026
Same author

An Engineered Core-Satellite Magnetic Aptasensor with Built-In Calibration and Regeneration for Accurate Quantification of Oral Cancer Exosomes.

ACS applied materials & interfaces·2026
Same author

Data-Driven Pressure Sensor Subset Selection for Long-Distance Water Transfer Pipelines: Q-DEIM Benchmarking with Spatial-Diversity Refinement.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: May 10, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

KG-bench: benchmarking graph neural network algorithms for drug repurposing.

Siqi Wei1, Christo Sasi1,2, Jelle Piepenbrock2

  • 1Department Medical BioSciences, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands.

Bioinformatics (Oxford, England)
|May 8, 2026
PubMed
Summary
This summary is machine-generated.

KG-Bench, a new framework, evaluates graph neural networks (GNNs) for drug repurposing. It benchmarks GNN performance in predicting drug-disease associations, aiding drug discovery.

Related Experiment Videos

Last Updated: May 10, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Area of Science:

  • Computational biology and cheminformatics
  • Drug discovery and development

Background:

  • Drug repurposing accelerates the identification of new therapeutic uses for existing drugs.
  • Computational methods integrating biological and chemical data are crucial for prioritizing drug repurposing candidates.
  • A lack of standardized benchmarks hinders the evaluation of deep learning models for drug-disease association prediction.

Purpose of the Study:

  • To introduce KG-Bench, a Graph Neural Network (GNN) benchmarking framework for evaluating drug-disease association prediction.
  • To systematically compare the performance of various GNN architectures using the Open Targets dataset.
  • To provide a standardized and reproducible evaluation framework for graph-based drug repurposing algorithms.

Main Methods:

  • Constructed a comprehensive knowledge graph (KG) integrating drugs, diseases, and targets with annotations like therapeutic area and molecular pathway.
  • Ensured retrospective validation by utilizing regular dataset updates and removed redundant entities across data splits to prevent data leakage.
  • Benchmarked six GNN architectures, including RGCN and TransformerConv, assessing performance metrics such as AUC and F1 score under class imbalance.

Main Results:

  • RGCN achieved the highest ranking performance with an AUC of 0.91.
  • TransformerConv demonstrated superior robustness in class-imbalanced scenarios (F1: 0.28 at 1:100 positive:negative ratio), relevant for real-world drug repurposing datasets.
  • KG-Bench incorporates assessments for bias, node/feature importance, and utilizes GNNExplainer for model interpretability.

Conclusions:

  • KG-Bench provides a standardized, open-source framework for fair and reproducible evaluation of GNNs in drug repurposing.
  • The framework facilitates systematic comparison of GNN architectures, aiding the selection of optimal models for drug-disease association prediction.
  • This work addresses the need for robust benchmarks in applying deep learning to accelerate drug discovery through repurposing.