#6026. Comparison of 16S and whole genome dog microbiomes using machine learning

July 2026publication date
Proposal available till 28-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Computational Mathematics;
Computational Theory and Mathematics;
Computer Science Applications;
Genetics;
Biochemistry;
Molecular Biology;
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Abstract:
Background: Recent advances in sequencing technologies have driven studies identifying the microbiome as a key regulator of overall health and disease in the host. Both 16S amplicon and whole genome shotgun sequencing technologies are currently being used to investigate this relationship, however, the choice of sequencing technology often depends on the nature and experimental design of the study. In principle, the outputs rendered by analysis pipelines are heavily influenced by the data used as input; it is then important to consider that the genomic features produced by different sequencing technologies may emphasize different results.
Keywords:
16S amplicon; Classification; Diabetes; Diet; Dog; Machine learning; Metagenomics; Microbiome; Supervised learning; Whole genome shotgun

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