Rate My Professor Ke Yuan

Dr. Ke Yuan is a Reader in Machine Learning and Computational Biology in the School of Computing Science at the University of Glasgow, with affiliate appointments in the School of Cancer Sciences and the CRUK Scotland Institute. He earned a BEng in Telecommunication Engineering from Nanjing University of Posts and Telecommunications in 2007, an MSc in Radio Frequency Communication Systems from the University of Southampton in 2008, and a PhD in Machine Learning from the same institution in 2012, advised by Professor Mahesan Niranjan. From 2012 to 2016, he served as a Postdoctoral Research Associate at the Cancer Research UK Cambridge Institute, University of Cambridge, collaborating with Dr. Florian Markowetz on computational models for cancer genomics. He joined the University of Glasgow in May 2016 as a Lecturer in Machine Learning and Computational Biology, promoted to Senior Lecturer in 2022, alongside secondment roles in cancer sciences and the CRUK Scotland Institute.

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.