MB-GAN: Microbiome Simulation via Generative Adversarial Network.

Journal: GigaScience
Published Date: Feb 5, 2021

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.

Authors

  • Ruichen Rong
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Shuang Jiang
    Research Center for Innovative Technology of Pharmaceutical Analysis, College of Pharmacy, Harbin Medical University, Harbin 150081 Heilongjiang, China.
  • Lin Xu
    Key Laboratory of Grain and Oil Processing and Food Safety of Sichuan Province, College of Food and Bioengineering, Xihua University Chengdu 610039 China xingyage1@163.com.
  • Guanghua Xiao
  • Yang Xie
    Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX, 75390, USA.
  • Dajiang J Liu
    Pennsylvania State University, Department of Public Health Sciences, 700 HMC Crescent Road, Hershey, PA 17033, USA.
  • Qiwei Li
    Department of General Surgery, South Campus, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
  • Xiaowei Zhan
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas. Electronic address: xiaowei.zhan@utsouthwestern.edu.

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