Graph Neural Networks for Gut Microbiome Metaomic data: A preliminary work

TL;DR

This paper explores the use of graph neural networks to analyze complex gut microbiome metaomic data, leveraging phylogenetic relationships to improve phenotype prediction like IBD diagnosis.

Contribution

It introduces a GNN-based approach that directly incorporates phylogenetic relationships for better microbiome representation and phenotype prediction.

Findings

GNNs effectively capture microbiome relationships.

Phylogenetic information improves prediction accuracy.

Preliminary results show promise for disease classification.

Abstract

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).