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The explosion of data throughout the biomedical sciences providesunprecedented opportunities to learn about the dynamics of evolution anddisease progression, but harnessing these large and diverse datasets remainschallenging. Here, we describe a highly generalisable statistical platform toinfer the dynamic pathways by which many, potentially interacting, discretetraits are acquired or lost over time in biomedical systems. The platform usesHyperTraPS (hypercubic transition path sampling) to learn progression pathwaysfrom cross-sectional, longitudinal, or phylogenetically-linked data withunprecedented efficiency, readily distinguishing multiple competing pathways,and identifying the most parsimonious mechanisms underlying given observations.Its Bayesian structure quantifies uncertainty in pathway structure and allowsinterpretable predictions of behaviours, such as which symptom a patient willacquire next. We exploit the model's topology to provide visualisation toolsfor intuitive assessment of multiple, variable pathways. We apply the method toovarian cancer progression and the evolution of multidrug resistance intuberculosis, demonstrating its power to reveal previously undetected dynamicpathways.

HyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways