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Bayesian modelling of Dupuytren disease using Gaussian copula graphical models

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SNAW02 - Network science and its applications

Co-authors: Fentaw Abegaz (University of Liege, Belgium), Edwin van den Heuvel (Eindhoven University of Technology, The Netherlands), Ernst Wit (University of Groningen, The Netherlands)

Dupuytren disease is a fibroproliferative disorder with unknown etiology that often progresses and eventually can cause permanent contractures of the affected fingers. In this talk, we provide a computationally efficient Bayesian framework to discover potential risk factors and investigate which fingers are jointly affected. Our Bayesian approach is based on Gaussian copula graphical models, which provide a way to discover the underlying conditional independence structure of variables in multivariate mixed data. In particular, we combine the semiparametric Gaussian copula with extended rank likelihood to analyse multivariate mixed data with arbitrary marginal distributions. For the structural learning, we construct a computationally efficient search algorithm using a trans-dimensional MCMC algorithm based on a birth-death process. In addition, to make our statistical method easily accessible to other researchers, we have implemented our method in C++ and provide an interface with R software as an R package BDgraph, which is freely available online. 

This talk is part of the Isaac Newton Institute Seminar Series series.

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