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

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 biological...

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Bibliographic Details
Main Authors: Daniel Ruiz-Perez, Isabella Gimon, Musfiqur Sazal, Kalai Mathee, Giri Narasimhan
Format: Article
Language:English
Published: American Society for Microbiology 2024-10-01
Series:mSystems
Subjects:
longitudinal microbiome analysis
multi-omic integration
causal inference
unfolding
de-confounding
Online Access:https://journals.asm.org/doi/10.1128/msystems.01303-23

Internet

https://journals.asm.org/doi/10.1128/msystems.01303-23

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