Synthetic DNA

Synthetic DNA research focuses on generating artificial DNA sequences for various applications, primarily driven by the need for large-scale, readily available genomic data for research and validation studies. Current research employs diverse machine learning approaches, including generative models like diffusion models and GANs, graph neural networks for analyzing gene interactions, and autoencoders for data compression and storage in synthetic DNA. These advancements are improving the accuracy and efficiency of synthetic DNA generation, impacting fields like genomics, medicine (e.g., personalized medicine and cancer research), and even raising concerns about potential misuse in biosecurity.