Using Conditional Generative Adversarial Networks to Boost the Performance of Machine Learning in Microbiome Datasets

Derek Reiman, Yang Dai

2020

Abstract

The microbiome of the human body has been shown to have profound effects on physiological regulation and disease pathogenesis. However, association analysis based on statistical modeling of microbiome data has continued to be a challenge due to inherent noise, complexity of the data, and high cost of collecting large number of samples. To address this challenge, we employed a deep learning framework to construct a data-driven simulation of microbiome data using a conditional generative adversarial network. Conditional generative adversarial networks train two models against each other while leveraging side information learn from a given dataset to compute larger simulated datasets that are representative of the original dataset. In our study, we used a cohorts of patients with inflammatory bowel disease to show that not only can the generative adversarial network generate samples representative of the original data based on multiple diversity metrics, but also that training machine learning models on the synthetic samples can improve disease prediction through data augmentation. In addition, we also show that the synthetic samples generated by this cohort can boost disease prediction of a different external cohort.

Paper Citation

in Harvard Style

Reiman D. and Dai Y. (2020). Using Conditional Generative Adversarial Networks to Boost the Performance of Machine Learning in Microbiome Datasets.In Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-441-1, pages 103-110. DOI: 10.5220/0009892601030110

in Bibtex Style

@conference{delta20,
author={Derek Reiman and Yang Dai},
title={Using Conditional Generative Adversarial Networks to Boost the Performance of Machine Learning in Microbiome Datasets},
booktitle={Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2020},
pages={103-110},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009892601030110},
isbn={978-989-758-441-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Using Conditional Generative Adversarial Networks to Boost the Performance of Machine Learning in Microbiome Datasets
SN - 978-989-758-441-1
AU - Reiman D.
AU - Dai Y.
PY - 2020
SP - 103
EP - 110
DO - 10.5220/0009892601030110