Stephanie Schmidt

The paper uses machine learning to identify distinct biological processes and druggable targets in early-onset prostate cancer compared to late-onset prostate cancer.

This paper introduces a graph-based model to identify druggable vulnerabilities in solid cancers, finding 43 novel targets by leveraging cooperativity in molecular networks.

The paper identifies IgnaviCas9, a hyperthermophilic Cas9 from an unculturable Ignavibacterium, which functions at higher temperatures than existing Cas9 variants and is applied in RNA-seq library preparation.