Matthew Amodio

AAnet identifies archetypal cell states in single-cell data to reveal tumor heterogeneity and spatial organization.

scMMGAN integrates multi-omic data to reveal spatial patterns in triple negative breast cancer, showing improved alignment and novel gene associations.

The paper introduces FMGAN, a conditional GAN that predicts hard-to-obtain biological information from easy-to-obtain data, demonstrating applications in drug discovery and clinical outcomes.

SAUCIE is a deep neural network that processes large single-cell datasets, performs batch correction, and identifies cluster-based signatures in dengue patient T cells.