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

• URL: http://arxiv.org/abs/2407.00142v1
• Date: Fri, 28 Jun 2024 15:53:36 GMT
• Title: Graph Neural Networks for Gut Microbiome Metaomic data: A preliminary work
• Authors: Christopher Irwin, Flavio Mignone, Stefania Montani, Luigi Portinale,
• Abstract summary: We aim to derive meaningful representations of individual gut microbiomes using graph neural networks (GNNs) The representation learnt from the encoder are then used to train a model for phenotype prediction such as Inflammatory Bowel Disease (IBD)
• Score: 0.47248250311484113
• License: http://creativecommons.org/licenses/by/4.0/
• 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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