Eldad D. Shulman

Jul 3, 2024 | nature.com | Danh-Tai Hoang |Eldad D. Shulman |Doreen S. Ben-Zvi |Sanju Sinha |Neelam Sinha |Christopher Dampier | +12 more

AbstractAdvances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT–DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values.