2020
DOI: 10.48550/arxiv.2007.01200
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A Semi-Supervised Generative Adversarial Network for Prediction of Genetic Disease Outcomes

Abstract: 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.… Show more

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