MB-GAN: Microbiome Simulation via Generative Adversarial Network.

ABSTRACT:

Background

Trillions of microbes inhabit the human body and have a profound effect on human health. The recent development of metagenome-wide association studies and other quantitative analysis methods accelerate the discovery of the associations between human microbiome and diseases. To assess the strengths and limitations of these analytical tools, simulating realistic microbiome datasets is critically important. However, simulating the real microbiome data is challenging because it is difficult to model their correlation structure using explicit statistical models.

Results

To address the challenge of simulating realistic microbiome data, we designed a novel simulation framework termed MB-GAN, by using a generative adversarial network (GAN) and utilizing methodology advanc

SUBMITTER: Rong R 

PROVIDER: S-EPMC7931821 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature