Rate My Professor Ke Yuan

Yuan's research integrates artificial intelligence with cancer biology, specializing in AI for histology and spatial deep phenotyping to analyze tumor microenvironments, large language models for proteins and RNA to predict mutation effects and interactions, and in silico tumor models for simulating treatment responses. His work encompasses cancer evolution, histomorphological phenotypes, drug-target interactions, single-cell image generation, and viral evolution. He has obtained significant funding, including grants from the Pathological Society of Great Britain and Ireland (2025-2026) for digital histomorphological biomarkers in lung cancer immunotherapy, Cancer Research UK (2024-2028) for AI-based classifiers in rectal cancer, and BBSRC (2022-2025) for vaccine design in infectious bronchitis virus. Notable publications include 'Pan-cancer analysis of whole genomes' (Nature, 2020), 'The evolutionary history of 2,658 cancers' (Nature, 2020), 'Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes' (Cell, 2021), 'Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides' (Nature Communications, 2024), and 'PLM-interact: extending protein language models to predict protein-protein interactions' (Nature Communications, 2025). Yuan contributes to the field as a program committee member for the Intelligent Systems for Molecular Biology (ISMB) 2024 Conference.