  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).
