Synthetic Data Generation of Gene Mutations in Leukemia Patients with Large Language Models
Gene mutations in patients diagnosed with Acute Myeloid Leukemia give invaluable insights on the best treatment to fight this aggressive disease. Generating realistic synthetic version of this data could help undercover biases and curate more robust and accessible datasets, which could yield to better understanding of the disease.
This work was done as the Master Thesis project at the University of Bologna, Applied Physics curriculum. The complete thesis can be read here. Most of the code developed for the study can be found on this GitHub repo. This work was part of a collaboration of the University of Bologna with Synthema, which aims to develop and validate Artificial Intelligence techniques for anonymisation and synthetic data generation in rare hematological diseases. If you have any question, don't hesitate to contact me at alessioferraioli@hotmail.it.
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