Variational autoencoders learn transferrable representations of metabolomics data

Variable autoencoders offer an alternative way to interrogate metabolomic data and identify meaningful, non-linear relationships.

Bibliographic Details
Main Authors:	Daniel P. Gomari, Annalise Schweickart, Leandro Cerchietti, Elisabeth Paietta, Hugo Fernandez, Hassen Al-Amin, Karsten Suhre, Jan Krumsiek
Format:	Article
Language:	English
Published:	Nature Portfolio 2022-06-01
Series:	Communications Biology
Online Access:	https://doi.org/10.1038/s42003-022-03579-3

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