PhylaGAN: data augmentation through conditional GANs and autoencoders for improving disease prediction accuracy using microbiome data.

ABSTRACT:

Motivation

Research is improving our understanding of how the microbiome interacts with the human body and its impact on human health. Existing machine learning methods have shown great potential in discriminating healthy from diseased microbiome states. However, Machine Learning based prediction using microbiome data has challenges such as, small sample size, imbalance between cases and controls and high cost of collecting large number of samples. To address these challenges, we propose a deep learning framework phylaGAN to augment the existing datasets with generated microbiome data using a combination of conditional generative adversarial network (C-GAN) and autoencoder. Conditional generative adversarial networks train two models against each other to compute larger simulated d

SUBMITTER: Sharma D 

PROVIDER: S-EPMC11256914 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature