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

Christopher Irwin, Flavio Mignone, Stefania Montani, Luigi Portinale
6/28/2024
cs.LGcs.AI

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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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Cite this paper

@article{irwin2024graph,
  title  = {Graph Neural Networks for Gut Microbiome Metaomic data: A preliminary work},
  author = {Christopher Irwin and Flavio Mignone and Stefania Montani and Luigi Portinale},
  year   = {2024},
  eprint = {2407.00142},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.00142v1}
}

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