Juliette C. Madan

This study investigates microbial dysbiosis in children with cystic fibrosis, identifying glycerol as a driver of altered microbial competition and linking colibactin production to increased colorectal cancer risk.

This study compares RNA extraction and preservation protocols for miRNA profiling in infant and toddler stool, finding that RNAlater and Zymo DNA/RNA Shield yield similar high-quality RNA with comparable human miRNA profiles.

This study profiles bile acids in stool from children with and without cystic fibrosis, revealing differences in BA species and microbial metabolism, and highlights the ferret model's suitability for BA research in CF.

The paper proposes a stationary Gaussian graphical model for microbial interaction networks using longitudinal 16S rRNA data and shows it outperforms conventional methods in correlated settings.

The paper introduces a method for selecting Gaussian graphical models using prior knowledge and applies it to metabolomic data, finding that metabolic pathways significantly influence metabolite dependencies.

The paper introduces a zero-inflated latent Ising model to estimate microbial interaction networks, showing it outperforms traditional methods in simulated and real gut microbiome data.

This study identifies altered stool microbiota in infants with cystic fibrosis, linking reduced Bacteroides and Bifidobacterium to immune-related metabolic pathways and systemic inflammation.