Musfiqur Rahman Sazal

The paper introduces METALICA, a tool for causal inference in microbiome data, to uncover interactions and confounders in longitudinal multi-omic datasets.

This paper introduces a signed Bayesian Network (sBN) to infer directional relationships in microbial communities using abundance data, finding that directed edges correlate with colonization order.

The paper introduces a method to analyze microbiome antibiotic response using replication rates and causal inference, finding microbes reduce replication under beta-lactams and specific resistance genes enable dominance.

This paper applies causal inference techniques to microbiome data to study antibiotic resistance, introducing a novel pipeline for predicting interventional effects without controlled experiments.