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

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Title: HyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways
Authors: Greenbury, S
Barahona, M
Johnston, I
Item Type: Journal Article
Abstract: The 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 generalisable statistical platform to infer the dynamic pathways by which many, potentially interacting, discrete traits are acquired or lost over time in biomedical systems. The platform uses HyperTraPS (hypercubic transition path sampling) to learn progression pathways from cross-sectional, longitudinal, or phylogenetically-linked data with unprecedented efficiency, readily distinguishing multiple competing pathways, and identifying the most parsimonious mechanisms underlying given observations. Its Bayesian structure quantifies uncertainty in pathway structure and allows interpretable predictions of behaviours, such as which symptom a patient will acquire next. We exploit the model’s topology to provide visualisation 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.
Issue Date: 22-Nov-2019
Date of Acceptance: 26-Oct-2019
URI: http://hdl.handle.net/10044/1/73878
DOI: 10.1016/j.cels.2019.10.009
ISSN: 2405-4712
Publisher: Elsevier (Cell Press)
Journal / Book Title: Cell Systems
Copyright Statement: © 2019 Elsevier Inc. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/N014529/1
Publication Status: Published online
Embargo Date: 2020-11-27
Online Publication Date: 2019-11-27
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Faculty of Medicine
Faculty of Natural Sciences



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