An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences

Synthetic data (SD) has become an increasingly important asset in the life sciences, helping address data scarcity, privacy concerns, and barriers to data access. Creating artificial datasets that mirror the characteristics of real data allows researchers to develop and validate computational methods in controlled environments. Despite its promise, the adoption of SD in life sciences hinges on rigorous evaluation metrics designed to assess their fidelity and reliability. To explore the current landscape of SD evaluation metrics in distinct life sciences domains, the ELIXIR Machine Learning Focus Group performed a systematic review of the scientific literature following the PRISMA guidelines. Six critical domains were examined to identify current practices for assessing SD. Findings reveal that, while generation methods are rapidly evolving, systematic evaluation is often overlooked, limiting researchers' ability to compare, validate, and trust synthetic datasets across different domains. This systematic review underscores the urgent need for robust, standardized evaluation approaches that not only bolster confidence in SD but also guide its effective and responsible implementation. By laying the groundwork for establishing domain-specific yet interoperable standards, this scoping review paves the way for future initiatives aimed at enhancing the role of SD in scientific discovery, clinical practice and beyond.

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Publication Details

Journal
NAR Genomics and Bioinformatics
Published
2026-01-06
DOI
https://doi.org/10.1093/nargab/lqag012
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
0.00

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article

An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences

Tom Lenaerts, Shaz Iqbal, Alfonso Valencia, Davide Cirillo et al.
NAR Genomics and Bioinformatics
Research Data Management Practices
article

An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences

Tom Lenaerts, Shaz Iqbal, Alfonso Valencia, Davide Cirillo, Lisa Crossman, Pilib Ó Broin, Barbara Gravel, Fotis Psomopoulos, Núria Queralt-Rosiñach, Styliani-Christina Fragkouli, Nagat Masued, Mark Onders, Devesh Haseja, Alex Stikkelman
article en

Abstract

Synthetic data (SD) has become an increasingly important asset in the life sciences, helping address data scarcity, privacy concerns, and barriers to data access. Creating artificial datasets that mirror the characteristics of real data allows researchers to develop and validate computational methods in controlled environments. Despite its promise, the adoption of SD in life sciences hinges on rigorous evaluation metrics designed to assess their fidelity and reliability. To explore the current landscape of SD evaluation metrics in distinct life sciences domains, the ELIXIR Machine Learning Focus Group performed a systematic review of the scientific literature following the PRISMA guidelines. Six critical domains were examined to identify current practices for assessing SD. Findings reveal that, while generation methods are rapidly evolving, systematic evaluation is often overlooked, limiting researchers' ability to compare, validate, and trust synthetic datasets across different domains. This systematic review underscores the urgent need for robust, standardized evaluation approaches that not only bolster confidence in SD but also guide its effective and responsible implementation. By laying the groundwork for establishing domain-specific yet interoperable standards, this scoping review paves the way for future initiatives aimed at enhancing the role of SD in scientific discovery, clinical practice and beyond.

NAR Genomics and BioinformaticsVol. 8(1)
University of Notre Dame (US), University of East Anglia (GB), Institució Catalana de Recerca i Estudis Avançats (ES), Université Libre de Bruxelles (BE), Vrije Universiteit Brussel (BE), Ollscoil na Gaillimhe – University of Galway (IE), National and Kapodistrian University of Athens (GR), Norwich Research Park (GB), Barcelona Supercomputing Center (ES), Leiden University Medical Center (NL), Centre for Research and Technology Hellas (GR), Universitat Politècnica de Catalunya (ES), University of Edinburgh (GB)
National Institute for Health and Care Excellence, European Federation of Pharmaceutical Industries and Associations, UK Research and Innovation, European Commission, Universiteit Leiden, Leids Universitair Medisch Centrum, Barcelona Supercomputing Center, HORIZON EUROPE Framework Programme
Openalex Percentile: Top 100%
Research Data Management Practices
DOIOpen Access PDFOpenAlex
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