Predicting microbiomes through a deep latent space | Zendy

Beatriz García-Jiménez | Zendy; J. A. Muñoz | Zendy; Sara Cabello | Zendy; Joaquı́n Medina | Zendy; Mark D. Wilkinson | Zendy

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Predicting microbiomes through a deep latent space

Author(s) -

Beatriz García-Jiménez,

J. A. Muñoz,

Sara Cabello,

Joaquı́n Medina,

Mark D. Wilkinson

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa971

Subject(s) - microbiome , computer science , deep learning , artificial intelligence , composition (language) , machine learning , biology , bioinformatics , linguistics , philosophy

Microbial communities influence their environment by modifying the availability of compounds, such as nutrients or chemical elicitors. Knowing the microbial composition of a site is therefore relevant to improve productivity or health. However, sequencing facilities are not always available, or may be prohibitively expensive in some cases. Thus, it would be desirable to computationally predict the microbial composition from more accessible, easily-measured features.

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