Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy.

ABSTRACT: The human gut microbiome is associated with a large number of disease etiologies. As such, it is a natural candidate for machine-learning-based biomarker development for multiple diseases and conditions. The microbiome is often analyzed using 16S rRNA gene sequencing or shotgun metagenomics. However, several properties of microbial sequence-based studies hinder machine learning (ML), including non-uniform representation, a small number of samples compared with the dimension of each sample, and sparsity of the data, with the majority of taxa present in a small subset of samples. We show here using a graph representation that the cladogram structure is as informative as the taxa frequency. We then suggest a novel method to combine information from different taxa and improve data representati

SUBMITTER: Shtossel O 

PROVIDER: S-EPMC10288916 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature