  For most diseases, building large databases of labeled genetic data is an
expensive and time-demanding task. To address this, we introduce genetic
Generative Adversarial Networks (gGAN), a semi-supervised approach based on an
innovative GAN architecture to create large synthetic genetic data sets
starting with a small amount of labeled data and a large amount of unlabeled
data. Our goal is to determine the propensity of a new individual to develop
the severe form of the illness from their genetic profile alone. The proposed
model achieved satisfactory results using real genetic data from different
datasets and populations, in which the test populations may not have the same
genetic profiles. The proposed model is self-aware and capable of determining
whether a new genetic profile has enough compatibility with the data on which
the network was trained and is thus suitable for prediction. The code and
datasets used can be found at https://github.com/caio-davi/gGAN.
