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Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations
BACKGROUND: Unsupervised compression algorithms applied to gene expression data extract latent or hidden signals representing technical and biological sources of variation. However, these algorithms require a user to select a biologically appropriate latent space dimensionality. In practice, most re...
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| Опубликовано в: : | Genome Biol |
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| Главные авторы: | , , , , |
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
BioMed Central
2020
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| Предметы: | |
| Online-ссылка: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7212571/ https://ncbi.nlm.nih.gov/pubmed/32393369 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s13059-020-02021-3 |
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