Supervised dimensionality reduction for big data

To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders of magnitude smaller than the...

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Detalles Bibliográficos
Autores principales: Vogelstein, Joshua T., Bridgeford, Eric W., Tang, Minh, Zheng, Da, Douville, Christopher, Burns, Randal, Maggioni, Mauro
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/PMC8129083/
https://www.ncbi.nlm.nih.gov/pubmed/34001899
http://dx.doi.org/10.1038/s41467-021-23102-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8129083/
https://www.ncbi.nlm.nih.gov/pubmed/34001899
http://dx.doi.org/10.1038/s41467-021-23102-2

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