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A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

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Published on: June 15, 2011

Data Augmentation and Synthetic Data Generation in Rare Disease Research: A Scoping Review.

Rebecca Finetti¹, Bianca Roncaglia¹, Anna Visibelli¹

¹Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy.

Medical Sciences (Basel, Switzerland)

|November 24, 2025

Summary

Data augmentation and synthetic data generation are crucial for rare disease research, expanding datasets and improving model robustness despite challenges. These methods can overcome data scarcity, driving innovation for more inclusive studies.

Keywords:

data augmentation machine learning rare diseases synthetic data

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Last Updated: Jan 10, 2026

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Published on: August 20, 2019

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Published on: June 13, 2025

Area of Science:

Medical Informatics
Bioinformatics
Computational Biology

Background:

Rare diseases pose significant research challenges due to limited data and small patient cohorts.
Heterogeneous phenotypes in rare diseases further complicate analysis and model development.
Data augmentation and synthetic data generation are emerging solutions to address data scarcity.

Conclusions:

Data augmentation and synthetic data generation successfully expanded datasets and enhanced model robustness.
Rigorous validation is essential to ensure the biological plausibility of generated data.
These techniques can transform data scarcity into an opportunity for methodological innovation in rare disease research.