Predicting microbiomes through a deep latent space.

ABSTRACT:

Motivation

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.

Results

Integrating deep learning techniques with microbiome data, we propose an artificial neural network architecture based on heterogeneous autoencoders to condense the long vector of microbial abundance values into a deep latent space representation. Then, we design a model to predict the deep late

SUBMITTER: Garcia-Jimenez B 

PROVIDER: S-EPMC8208755 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature