Derek J. Reiman

MiMeNet: Exploring Microbiome-Metabolome Relationships using Neural Networks

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: Metagenomic Signature Identifier based on Rank Aggregation of Features

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

Inferring cellular trajectories from scRNA-seq using Pseudocell Tracer

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: A Phylogenetic Tree Embedded Architecture for Convolution Neural Networks for Metagenomic Data

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

Using Conditional Generative Adversarial Networks to Boost the Performance of Machine Learning in Microbiome Datasets

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