Variational autoencoders learn transferrable representations of metabolomics data

Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional metabolomics datasets into underlying core metabolic processes. However, current state-of-the-art methods are widely incapable of detecting nonlinearities in metabolomics data. Variational Autoencoders (V...

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Detalles Bibliográficos
Autores principales: Gomari, Daniel P., Schweickart, Annalise, Cerchietti, Leandro, Paietta, Elisabeth, Fernandez, Hugo, Al-Amin, Hassen, Suhre, Karsten, Krumsiek, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9246987/
https://www.ncbi.nlm.nih.gov/pubmed/35773471
http://dx.doi.org/10.1038/s42003-022-03579-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9246987/
https://www.ncbi.nlm.nih.gov/pubmed/35773471
http://dx.doi.org/10.1038/s42003-022-03579-3

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