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Predicting microbiome compositions through deep learning

View ORCID ProfileSebastian Michel-Mata, Xu-Wen Wang, View ORCID ProfileYang-Yu Liu, Marco Tulio Angulo
doi: https://doi.org/10.1101/2021.06.17.448886
Sebastian Michel-Mata
1Center for Applied Physics and Advanced Technology, Universidad Nacional Autónoma de México, Juriquilla 76230, México
2Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
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  • ORCID record for Sebastian Michel-Mata
Xu-Wen Wang
3Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
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Yang-Yu Liu
3Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
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  • For correspondence: mangulo@im.unam.mx yyl@channing.harvard.edu
Marco Tulio Angulo
4CONACyT - Institute of Mathematics, Universidad Nacional Autónoma de México, Juriquilla 76230, México
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  • For correspondence: mangulo@im.unam.mx yyl@channing.harvard.edu
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Abstract

Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment1,2 or the well-being of their hosts3–6. Successfully managing these microbial communities requires the ability to predict the community composition based on the species assemblage7. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical8, biochemical9, and ecological10,11 processes governing the microbial dynamics. To overcome this challenge, here we present a deep learning framework that automatically learns the map between species assemblages and community compositions from training data. First, we systematically validate our framework using synthetic data generated by classical population dynamics models. Then, we apply it to experimental data of both in vitro and in vivo communities, including ocean and soil microbial communities12,13, Drosophila melanogaster gut microbiota14, and human gut and oral microbiota15. Our results demonstrate how deep learning can enable us to understand better and potentially manage complex microbial communities.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 17, 2021.
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Predicting microbiome compositions through deep learning
Sebastian Michel-Mata, Xu-Wen Wang, Yang-Yu Liu, Marco Tulio Angulo
bioRxiv 2021.06.17.448886; doi: https://doi.org/10.1101/2021.06.17.448886
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Predicting microbiome compositions through deep learning
Sebastian Michel-Mata, Xu-Wen Wang, Yang-Yu Liu, Marco Tulio Angulo
bioRxiv 2021.06.17.448886; doi: https://doi.org/10.1101/2021.06.17.448886

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