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For most diseases, building large databases of labeled genetic data is anexpensive and time-demanding task. To address this, we introduce geneticGenerative Adversarial Networks (gGAN), a semi-supervised approach based on aninnovative GAN architecture to create large synthetic genetic data setsstarting with a small amount of labeled data and a large amount of unlabeleddata. Our goal is to determine the propensity of a new individual to developthe severe form of the illness from their genetic profile alone. The proposedmodel achieved satisfactory results using real genetic data from differentdatasets and populations, in which the test populations may not have the samegenetic profiles. The proposed model is self-aware and capable of determiningwhether a new genetic profile has enough compatibility with the data on whichthe network was trained and is thus suitable for prediction. The code anddatasets used can be found at https://github.com/caio-davi/gGAN.

A Semi-Supervised Generative Adversarial Network for Prediction of Genetic Disease Outcomes