Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions

Existing computational methods that use single-cell RNA-sequencing (scRNA-seq) for cell fate prediction do not model how cells evolve stochastically and in physical time, nor can they predict how differentiation trajectories are altered by proposed interventions. We introduce PRESCIENT (Potential en...

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Detalles Bibliográficos
Autores principales: Yeo, Grace Hui Ting, Saksena, Sachit D., Gifford, David K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163769/
https://www.ncbi.nlm.nih.gov/pubmed/34050150
http://dx.doi.org/10.1038/s41467-021-23518-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163769/
https://www.ncbi.nlm.nih.gov/pubmed/34050150
http://dx.doi.org/10.1038/s41467-021-23518-w

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