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dc.contributor.authorCarrillo Pérez, Francisco 
dc.contributor.authorHerrera Maldonado, Luis Javier 
dc.date.accessioned2023-10-24T11:00:58Z
dc.date.available2023-10-24T11:00:58Z
dc.date.issued2023-08-28
dc.identifier.citationCarrillo-Perez et al., 2023, Cell Reports Methods 3, 100534 August 28, 2023 ª 2023 The Author(s). [https://doi.org/10.1016/j.crmeth.2023.100534]es_ES
dc.identifier.urihttps://hdl.handle.net/10481/85217
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: https://rna-gan.stanford.edu/, and the code for RNA-GAN is available here: https://github.com/gevaertlab/RNA-GAN.es_ES
dc.description.sponsorshipGrants PID2021- 128317OB-I00es_ES
dc.description.sponsorshipMCIN/AEI/10.13039/501100011033es_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.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleSynthetic whole-slide image tile generation with gene expression profile-infused deep generative modelses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1016/j.crmeth.2023.100534
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_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