Synthetic data for pharmacogenetics: enabling scalable and secure research.

ABSTRACT:

Objective

This study evaluates the performance of 7 synthetic data generation (SDG) methods-synthpop, avatar, copula, copulagan, ctgan, tvae, and the large language models-based tabula-for supporting pharmacogenetics (PGx) research.

Materials and methods

We used PGx profiles from 142 patients with adverse drug reactions or therapeutic failures, considering 2 scenarios: (1) a high-dimensional genotype dataset (104 variables) and (2) a phenotype dataset (24 variables). Models were assessed for (1) broad utility using propensity score mean squared error ( pMSE ), (2) specific utility via weighted F1 score in a Train-Synthetic-Test-Real framework, and (3) privacy risk as ε-identifiability.

Results

Copula and synthpop consistently achieved strong performance across both da

SUBMITTER: Miletic M 

PROVIDER: S-EPMC12492482 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature