Discover immunotherapy biomarkers from single-cell cytometry data

Currently, identifying novel biomarkers remains a crucial need for cancer immunotherapy. By leveraging single-cell cytometry data, Greene et al. developed an interpretable machine learning method, FAUST, to discover cell populations associated with clinical outcomes.

Detalles Bibliográficos
Autores principales: Ru, Beibei, Jiang, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672134/
https://www.ncbi.nlm.nih.gov/pubmed/34950905
http://dx.doi.org/10.1016/j.patter.2021.100384
Descripción
Sumario:Currently, identifying novel biomarkers remains a crucial need for cancer immunotherapy. By leveraging single-cell cytometry data, Greene et al. developed an interpretable machine learning method, FAUST, to discover cell populations associated with clinical outcomes.

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