Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa.

ABSTRACT: Machine learning-based classification approaches are widely used to predict host phenotypes from microbiome data. Classifiers are typically employed by considering operational taxonomic units or relative abundance profiles as input features. Such types of data are intrinsically sparse, which opens the opportunity to make predictions from the presence/absence rather than the relative abundance of microbial taxa. This also poses the question whether it is the presence rather than the abundance of particular taxa to be relevant for discrimination purposes, an aspect that has been so far overlooked in the literature. In this paper, we aim at filling this gap by performing a meta-analysis on 4,128 publicly available metagenomes associated with multiple case-control studies. At species-level tax

SUBMITTER: Giliberti R 

PROVIDER: S-EPMC9064115 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature