Derek J. Reiman

TRIBAL infers B cell lineage trees from scRNA-seq data by integrating isotype information, improving accuracy in capturing somatic mutations and class switch dynamics.

MiMeNet is a neural network framework that improves prediction of metabolite abundances and identifies microbe-metabolite interactions, outperforming linear models in paired microbiome-metabolome datasets.

Meta-Signer identifies metagenomic signatures for disease association by aggregating feature rankings from multiple machine learning models.

Pseudocell Tracer infers cellular trajectories from scRNA-seq data by simulating pseudocells in a virtual cell-state axis, revealing transcription factor dynamics during B cell immunoglobulin class switch recombination.

PopPhy-CNN uses a phylogenetic tree embedded architecture to predict host phenotype from metagenomic data, outperforming traditional and baseline models.

This study uses conditional generative adversarial networks to simulate microbiome data, improving disease prediction through synthetic data augmentation in inflammatory bowel disease.