Tyler Matsuzaki

The paper uses a deep learning framework to analyze drug-induced transcriptomics and identifies a four-node module involved in TGFβ-IFNα competition in MASH.

The paper develops a machine learning framework to identify optimal experimental conditions for improving the translatability of microphysiological systems to in vivo contexts, using MAFLD as a case study.

The paper identifies transcriptional drift in aging C. elegans worms, showing upregulated sensory proteins and downregulated growth/metabolism genes, and proposes a mechanistic model involving ligand binding.