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MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks
Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on single-cell gene expression data. We also develop MichiGAN...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139054/ https://www.ncbi.nlm.nih.gov/pubmed/34016135 http://dx.doi.org/10.1186/s13059-021-02373-4 |