EVENFLOW has released its second white paper, The Personalised Medicine Use Case of EVENFLOW, authored by Guillermo Prol-Castelo, Davide Cirillo, and Alfonso Valencia. The paper presents how synthetic data can support personalised cancer treatments.

The work showcases a Variational Autoencoder (VAE)–based approach to generate reliable synthetic biomedical data, addressing data scarcity in underrepresented patient groups and temporal reconstruction challenges.

Two cancer case studies are highlighted:

• Medulloblastoma: Synthetic samples helped characterise an intermediate and underrepresented subgroup (G3–G4), improving classification and supporting more precise treatment decisions.
• Kidney Cancer: Synthetic disease-progression trajectories were generated to model how cancer evolves from early to late stages, validated through biological and computational analyses.

This white paper underscores the potential of synthetic data to enhance biomedical research when real-world data are limited and reflects EVENFLOW’s commitment to responsible AI in healthcare.

Read the white paper here!