Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks

A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher rep...

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Detalles Bibliográficos
Autores principales: Marouf, Mohamed, Machart, Pierre, Bansal, Vikas, Kilian, Christoph, Magruder, Daniel S., Krebs, Christian F., Bonn, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952370/
https://www.ncbi.nlm.nih.gov/pubmed/31919373
http://dx.doi.org/10.1038/s41467-019-14018-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952370/
https://www.ncbi.nlm.nih.gov/pubmed/31919373
http://dx.doi.org/10.1038/s41467-019-14018-z

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