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Interpretable generative deep learning: an illustration with single cell gene expression data
Deep generative models can learn the underlying structure, such as pathways or gene programs, from omics data. We provide an introduction as well as an overview of such techniques, specifically illustrating their use with single-cell gene expression data. For example, the low dimensional latent repr...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer Berlin Heidelberg
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9360114/ https://www.ncbi.nlm.nih.gov/pubmed/34988661 http://dx.doi.org/10.1007/s00439-021-02417-6 |