Translational research of many disease areas requires a longitudinal understanding of disease development and progression across all biologically relevant scales. Several corresponding studies are now available. However, to compile a comprehensive picture of a specific disease, multiple studies need to be analyzed and compared. A large number of clinical studies is nowadays conducted in the context of drug development in pharmaceutical research.
However, legal and ethical constraints typically do not allow for sharing sensitive patient data. In consequence there exist data “silos”, which slow down the overall scientific progress in translational research.
In this paper, the authors suggest the idea of a virtual cohort (VC) to address this limitation. Their key idea is to describe a longitudinal patient cohort with the help of a generative statistical model, namely a modular Bayesian Network, in which individual modules are represented as sparse autoencoder networks. They show that with the help of such a model they can simulate subjects that are highly similar to real ones. Their approach allows for incorporating arbitrary multi-scale, multi-modal data without making specific distribution assumptions. Moreover, they demonstrate the possibility to simulate interventions (e.g. via a treatment) in the VC. Overall, their proposed approach opens the possibility to build sufficiently realistic VCs for multiple disease areas in the future.
The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement 115568, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution.
Read the full paper here: https://www.nature.com/articles/s41598-020-67398-4
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