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dc.contributor.authorCarrillo Pérez, Francisco 
dc.contributor.authorHerrera Maldonado, Luis Javier
dc.identifier.citationCarrillo-Perez et al., 2023, Cell Reports Methods 3, 100534 August 28, 2023 ª 2023 The Author(s). []es_ES
dc.description.abstractIn this work, we propose an approach to generate whole-slide image (WSI) tiles by using deep generative models infused with matched gene expression profiles. First, we train a variational autoencoder (VAE) that learns a latent, lower-dimensional representation of multi-tissue gene expression profiles. Then, we use this representation to infuse generative adversarial networks (GANs) that generate lung and brain cortex tissue tiles, resulting in a new model that we call RNA-GAN. Tiles generated by RNA-GAN were preferred by expert pathologists compared with tiles generated using traditional GANs, and in addition, RNA-GAN needs fewer training epochs to generate high-quality tiles. Finally, RNA-GAN was able to generalize to gene expression profiles outside of the training set, showing imputation capabilities. A web-based quiz is available for users to play a game distinguishing real and synthetic tiles:, and the code for RNA-GAN is available here:
dc.description.sponsorshipGrants PID2021- 128317OB-I00es_ES
dc.description.sponsorshipProject P20-00163, funded by Consejerı´a de Universidad, Investigacio´ n e Innovacioes_ES
dc.description.sponsorshipERDF A way of making Europees_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.titleSynthetic whole-slide image tile generation with gene expression profile-infused deep generative modelses_ES

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional