Synthetic Genomic and Omics Data Applications in Precision Medicine and Biomedical Research

Synthetic genomic and omics datasets, encompassing genomics, transcriptomics, proteomics, and metabolomics, enable researchers to replicate the complex biological variability of real-world data while safeguarding sensitive patient information. Leveraging advanced generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion-based frameworks, scientists can now create realistic artificial datasets suitable for algorithm training, model validation, and cross-institutional collaboration. This chapter explores the principles, methodologies, and applications of synthetic genomic and omic data, emphasizing their transformative role in personalized medicine, drug discovery, and clinical trial simulation. It also discusses ethical, technical, and regulatory challenges, proposing future research directions toward multi-omics integration, privacy-preserving architectures, and global data-sharing frameworks.

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