Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA
Daniel Ruiz-Perez, Isabella Gimon, Musfiqur Rahman Sazal, Kalai Mathee, Giri Narasimhan
The paper introduces METALICA, a tool for causal inference in microbiome data, to uncover interactions and confounders in longitudinal multi-omic datasets.
mSystems · Sep 6, 2024Peer-reviewedBioinformatics