GAN-GMHI: Generative Adversarial Network for high discrimination power in microbiome-based disease prediction

Abstract

Summary: Gut microbiome-based health index (GMHI) has been applied with success, while the discrimination powers of GMHI varied for different diseases, limiting its utility on a broad spectrum of diseases. In this work, a Generative Adversarial Network (GAN) model is proposed to improve the discrimination power of GMHI. Built based on the batch corrected data through GAN (https://github.com/HUST-NingKang-Lab/GAN-GMHI), GAN-GMHI has largely reduced the batch effects, and profoundly improved the performance for distinguishing healthy individuals and different diseases. GAN-GMHI has provided results to support the strong association of gut microbiome and diseases, and indicated a more accurate venue towards microbiome-based disease monitoring. Availability and implementation: GAN-GMHI is publicly available on GitHub: https://github.com/HUST-NingKang-Lab/GAN-GMHI. Contact: ningkang@hust.edu.cn