CACONET: a novel classification framework for microbial correlation networks.

ABSTRACT:

Motivation

Existing microbiome-based disease prediction relies on the ability of machine learning methods to differentiate disease from healthy subjects based on the observed taxa abundance across samples. Despite numerous microbes have been implicated as potential biomarkers, challenges remain due to not only the statistical nature of microbiome data but also the lack of understanding of microbial interactions which can be indicative of the disease.

Results

We propose CACONET (classification of Compositional-Aware COrrelation NETworks), a computational framework that learns to classify microbial correlation networks and extracts potential signature interactions, taking as input taxa relative abundance across samples and their health status. By using Bayesian compositional-a

SUBMITTER: Xu Y 

PROVIDER: S-EPMC8896646 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature