Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA.

ABSTRACT: A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing to

SUBMITTER: Ruiz-Perez D 

PROVIDER: S-EPMC11494969 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature