Contextualized Networks Reveal Heterogeneous Transcriptomic Regulation in Tumors at Sample-Specific Resolution is online now on bioRxiv. We introduce a new machine learning paradigm for sample-specific inference of probabilistic graphical models, and show that sample-specific models of transcriptional regulation are highly accurate, interpretable, and prognostic across 25 tumor types and nearly 8000 patints. I’ll be sharing significant updates to the method and results at NeurIPS 2023 GenBio Workshop.