Article

Variational autoencoders learn universal latent representations of metabolomics data

2021-01-17

Abstract excerpt

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 (VAEs) are a deep learning method designed to learn nonlinear latent representations which generalize to unseen data. H...

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Literature Corpus work: be609f03-0d7e-5f02-86d1-faa3b82db6ca
DOI: 10.1101/2021.01.14.426721

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