Cancer Biology workshops

Mechanistic cancer models and data-driven learning offer complementary ways to understand tumour behaviour and improve individualized treatment. This workshop explores how mathematical modelling can be combined with machine and deep learning, bringing together data scientists and mathematical oncologists. Plenary lectures survey current developments, while collaborative group projects develop algorithms that integrate mechanistic knowledge with observations. The programme aims to turn methodological exchange into sustained collaborations and more informative approaches to personalized cancer care.

Researchers learn the assumptions and implementation principles behind machine-learning models used in cancer genomics. The five-day programme develops hidden Markov approaches for copy-number inference, mixture models for tumour subclones, hierarchical models for mutation signals and autoencoders for single-cell data. Lectures alternate with discussion, coding practice and a final project, emphasizing inference of latent biological states from noisy or incomplete measurements. Participants may discuss suitable datasets they own. Online teaching runs 14:00–19:00, Monday through Friday.