Response to Joob and Wiwanitkit Re: “AA <i>IDO1</i> Variant Genotype (G2431A, rs3739319) Is Associated with Severe Dengue Risk Development in a DEN-3 Brazilian Cohort”

Azevedo, Beatriz Peixoto; Farias, Pablo Cantalice Santos; Pastor, André Filipe; Davi, Caio; Neco, Heytor Victor Pereira da Costa; Lima, Raul Emídio de; Acioli-Santos, Bartolomeu

doi:10.1089/vim.2019.0098

Cited by 1 publication

(1 citation statement)

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“…There is evidence that some individuals may be genetically predisposed to some prognostic of the Dengue disease [6], [7]. More specifically, studies have found associations between the genetic expression, mostly in the innate and in the adaptive response pathways, and the Dengue infection phenotype [14]- [18]. Also, there are efforts to use Machine Learning techniques to find associations between static genetic profiles and dengue severity [10], [19].…”

Section: Introductionmentioning

confidence: 99%

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

Davi¹,

Braga-Neto²

2020

Preprint

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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 selfaware 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 (suppressed in anonymous version) https://github.com/caio-davi/gGAN

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