Author

Hugo Francisco Fernandez

Memorial Healthcare SystemORCID 0000-0002-7322-0392Unclaimed ORCID profile · follow to get notified of new preprints

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Organism:human· 1 Assay:metabolomics· 1 Approach:variational autoencoder· 1

Frequent Co-Authors

Annalise SchweickartiD· 1 shared Daniel P. Gomari· 1 shared Elisabeth M. PaiettaiD· 1 shared Hassen A. Al-AminiD· 1 shared Jan KrumsiekiD· 1 shared Karsten SuhreiD· 1 shared Leandro C. A. CerchiettiiD· 1 shared

Activity by Year

2021-2021 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Annalise SchweickartORCID

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1 paper

Recent·Most cited

Variational autoencoders learn universal latent representations of metabolomics data

Daniel P. Gomari, Annalise Schweickart, Leandro C. A. Cerchietti, Elisabeth M. Paietta, Hugo Francisco Fernandez, Hassen A. Al-Amin, Karsten Suhre, Jan Krumsiek

This study trains a variational autoencoder on metabolomics data to learn nonlinear latent representations and validates its generalizability across different disease cohorts.

bioRxiv · Jan 14, 2021 · PreprintBioinformatics