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

2024/06/28 by Christopher Irwin, Irwin, Christopher, Flavio Mignone +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Diet and metabolism studies #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Metabolomics and Mass Spectrometry Studies

paper · pdf · doi:10.48550/arxiv.2407.00142

openalex publication_date 2024/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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).

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