Graph Neural Networks for Gut Microbiome Metaomic data: A preliminary work
This work addresses the problem of high-dimensional and sparse gut microbiome data analysis for health applications, but it appears incremental as it applies GNNs to a specific domain without claiming major breakthroughs.
The paper tackled the challenge of analyzing complex gut microbiome metaomic data by investigating graph neural networks (GNNs) to derive meaningful representations, aiming to use these for phenotype prediction like Inflammatory Bowel Disease (IBD).
The gut microbiome, crucial for human health, presents challenges in analyzing its complex metaomic data due to high dimensionality and sparsity. Traditional methods struggle to capture its intricate relationships. We investigate graph neural networks (GNNs) for this task, aiming to derive meaningful representations of individual gut microbiomes. Unlike methods relying solely on taxa abundance, we directly leverage phylogenetic relationships, in order to obtain a generalized encoder for taxa networks. The representation learnt from the encoder are then used to train a model for phenotype prediction such as Inflammatory Bowel Disease (IBD).