HyperTraPS: Learning evolutionary and disease progression pathways from large-scale biological and medical datasets

The explosion of data throughout the biological and medical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging. I will talk about HyperTraPS (hypercubic transition path sampling), a highly generalisable Bayesian approach which efficiently learns evolutionary and progression pathways from cross-sectional, longitudinal, or phylogenetically linked data. I will discuss applications to severe malaria, ovarian cancer progression, and the evolution of multidrug resistance in tuberculosis, demonstrating the power of HyperTraPS to reveal previously  undetected dynamic pathways and make predictions of potential clinical value.