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dc.contributor.authorGreenbury, Sam
dc.contributor.authorBarahona, Mauricio
dc.contributor.authorJohnston, Iain
dc.PublishedGreenbury S, Barahona M, Johnston I G. HyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways. Cell Systems. 2020;10(1):39-51eng
dc.description.abstractThe explosion of data throughout the biomedical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging. Here, we describe a highly generalizable statistical platform to infer the dynamic pathways by which many, potentially interacting, traits are acquired or lost over time. We use HyperTraPS (hypercubic transition path sampling) to efficiently learn progression pathways from cross-sectional, longitudinal, or phylogenetically linked data, readily distinguishing multiple competing pathways, and identifying the most parsimonious mechanisms underlying given observations. This Bayesian approach allows inclusion of prior knowledge, quantifies uncertainty in pathway structure, and allows predictions, such as which symptom a patient will acquire next. We provide visualization tools for intuitive assessment of multiple, variable pathways. We apply the method to ovarian cancer progression and the evolution of multidrug resistance in tuberculosis, demonstrating its power to reveal previously undetected dynamic pathways.en_US
dc.publisherCell Pressen_US
dc.rightsAttribution-NonCommercial-NoDerivs CC BY-NC-ND
dc.titleHyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathwaysen_US
dc.typePeer reviewed
dc.typeJournal article
dc.rights.holderCopyright 2019 Elsevieren_US
dc.source.journalCell Systems
dc.identifier.citationCell Systems. 2020;10(1):39-51

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