Jove
Visualize
Contact Us

Related Concept Videos

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

677
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...
677
Synthetic Biology02:55

Synthetic Biology

5.5K
Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
5.5K
Genetic Screens02:46

Genetic Screens

5.6K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
5.6K

You might also read

Related Articles

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

Sort by
Same author

Pharmacy staff perception of the suitability of an app-based medication adherence service and strategies for implementation - A Swiss national survey.

Digital health·2026
Same author

Enhancing Discretion and Consistency in Emergency Contraception Counselling: Implementation of a Digital Support Tool in Community Pharmacies.

Pharmacy (Basel, Switzerland)·2026
Same author

Population-Based Assessment of Phenoconversion Potential in Switzerland: A Claims Data Study of Key Drug-Metabolizing Enzymes and Transporters.

Pharmacogenomics and personalized medicine·2026
Same author

Biomarker-Based Prediction of OATP1B1 Activity in Clinical Routine-Investigating Coproporphyrins as Markers for Drug-Drug-Gene Interactions.

Clinical pharmacology and therapeutics·2026
Same author

Patient Preferences Towards Plain Language Resources During their Multiple Sclerosis Journey: A Qualitative Interview Study.

The patient·2026
Same author

Genetic Determinants of Analgesic Responsiveness: A Focus on CYP2D6 and COMT Polymorphisms in Chronic Pain.

The Clinical journal of pain·2026
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 Experiment Video

Updated: Jan 16, 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

10.2K

Synthetic data for pharmacogenetics: enabling scalable and secure research.

Marko Miletic1, Anna Bollinger2, Samuel S Allemann2

  • 1Institute for Optimisation and Data Analysis (IODA), Bern University of Applied Sciences, Biel, Switzerland.

JAMIA Open
|October 6, 2025
PubMed
Summary

For pharmacogenetics research, traditional synthetic data generation methods like copula and synthpop provide a strong balance of data utility and privacy protection, outperforming deep learning models in many scenarios.

Keywords:
artificial intelligence in healthcaredata privacygenomic datapharmacogeneticssynthetic data

More Related Videos

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.5K
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

900

Related Experiment Videos

Last Updated: Jan 16, 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

10.2K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.5K
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

900

Area of Science:

  • Pharmacogenetics
  • Bioinformatics
  • Data Science

Background:

  • Synthetic data generation (SDG) is crucial for pharmacogenetics (PGx) research, especially with limited or sensitive patient data.
  • Evaluating various SDG methods is essential to determine their suitability for complex PGx datasets.
  • Assessing both data utility and privacy is critical for responsible data sharing and research.

Purpose of the Study:

  • To evaluate the performance of seven synthetic data generation (SDG) methods for pharmacogenetics (PGx) research.
  • To compare traditional and deep learning-based SDG approaches on high-dimensional genotype and phenotype PGx data.
  • To assess SDG methods based on broad utility, specific utility, and privacy risk.

Main Methods:

  • Seven SDG methods (synthpop, avatar, copula, copulagan, ctgan, tvae, tabula) were evaluated.
  • PGx profiles from 142 patients were used, with scenarios including high-dimensional genotype (104 variables) and phenotype (24 variables) data.
  • Performance was assessed using propensity score mean squared error (pMSE) for broad utility, weighted F1 score for specific utility, and ε-identifiability for privacy risk.

Main Results:

  • Copula and synthpop demonstrated consistent strong performance, balancing low privacy risk (ε-identifiability: 0.25-0.35) with competitive utility.
  • Deep learning models (tabula, tvae) achieved lower pMSE but had higher privacy risks (>0.4) and limited predictive gains.
  • Specific utility (F1 score) was weakly correlated with broad utility (pMSE), indicating distributional fidelity does not guarantee predictive relevance.

Conclusions:

  • No single SDG method excelled across all evaluation criteria.
  • For privacy-sensitive PGx research, copula and synthpop offer a reliable trade-off between utility and privacy, particularly for high-dimensional, limited-sample datasets.
  • Multimetric evaluation is essential, as general utility metrics do not always predict specific predictive utility.