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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2407.00142 |
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| _version_ | 1866912443359297536 |
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| author | Irwin, Christopher Mignone, Flavio Montani, Stefania Portinale, Luigi |
| author_facet | Irwin, Christopher Mignone, Flavio Montani, Stefania Portinale, Luigi |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00142 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Networks for Gut Microbiome Metaomic data: A preliminary work Irwin, Christopher Mignone, Flavio Montani, Stefania Portinale, Luigi Machine Learning Artificial Intelligence 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). |
| title | Graph Neural Networks for Gut Microbiome Metaomic data: A preliminary work |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2407.00142 |