<p>Condensed microbiome representation using transfer and deep learning to promote microbial composition prediction</p>

Sara Cabello; Beatriz García-Jiménez; Mark D. Wilkinson

doi:10.7490/f1000research.1117779.1

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Cabello S, García-Jiménez B and Wilkinson MD. Condensed microbiome representation using transfer and deep learning to promote microbial composition prediction [version 1; not peer reviewed]. F1000Research 2019, 8(ISCB Comm J):2118 (poster) (https://doi.org/10.7490/f1000research.1117779.1)

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Condensed microbiome representation using transfer and deep learning to promote microbial composition prediction

Sara Cabello, Beatriz García-Jiménez¹, Mark D. Wilkinson

Published 18 Dec 2019 (https://doi.org/10.7490/f1000research.1117779.1)

Author Affiliations

¹ Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA) Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Spain

Copyright © 2019 Cabello S et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 18 Dec 2019

Condensed microbiome representation using transfer and deep learning to promote microbial composition prediction
[version 1; not peer reviewed]

Sara Cabello, Beatriz García-Jiménez¹, Mark D. Wilkinson

Author Affiliations

¹ Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA) Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Spain

Presented at

Advances in Computational Biology 2019

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Microbiome, deep learning, transfer learning, Autoencoder, dimensionality reduction, artificial intelligence, latent space

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