ArticleFrontiers in genetics2021
G2S: A New Deep Learning Tool for Predicting Stool Microbiome Structure From Oral Microbiome Data.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
- Regulatory effects of oral microbe on intestinal microbiota and the illness.Frontiers in cellular and infection microbiology · 2023Review
- From the nose to the lungs: the intricate journey of airborne pathogens amid commensal bacteria.American journal of physiology. Cell physiology · 2022Review
- Over-feeding the gut microbiome: A scoping review on health implications and therapeutic perspectives.World journal of gastroenterology · 2021Article
- BowSaw: Inferring Higher-Order Trait Interactions Associated With Complex Biological Phenotypes.Frontiers in molecular biosciences · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning methodologies have revolutionized prediction in many fields and show the potential to do the same in microbial metagenomics. However, deep learning is still unexplored in the field of microbiology, with only a few software designed to work with microbiome data. Within the meta-community theory, we foresee new perspectives for the development and application of deep learning algorithms in the field of the human microbiome. In this context, we developed G2S, a bioinformatic tool for taxonomic prediction of the human fecal microbiome directly from the oral microbiome data of the same individual. The tool uses a deep convolutional neural network trained on paired oral and fecal samples from populations across the globe, which allows inferring the stool microbiome at the family level more accurately than other available approaches. The tool can be used in retrospective studies, where fecal sampling was not performed, and especially in the field of paleomicrobiology, as a unique opportunity to recover data related to ancient gut microbiome configurations. G2S was validated on already characterized oral and fecal sample pairs, and then applied to ancient microbiome data from dental calculi, to derive putative intestinal components in medieval subjects.
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Registered trials
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