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​​Time-varying dynamic Bayesian network reconstruction with information sharing​

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If you have a question about this talk, please contact Konstantina Palla .

​Network reconstruction is a well-studied topic, with applications in systems biology, pathway medicine and social science among other areas. In many of the most interesting problems, the network structure does not remain static over time, but changes as a result of environmental factors or outside interventions. Thus we require network reconstruction techniques that can deal with time-varying networks. In this talk, I present a technique based on dynamic Bayesian networks with change points. The network structure, parameters, and the changepoints are all inferred from the data using reversible-jump MCMC . In addition, we use information sharing priors to leverage the commonalities in network structure between adjacent time epochs. The model is applied to two real-world examples of changing networks; a network of muscle development genes in drosophila during morphogenesis, and the IRMA synthetic biology gene network that can be turned on or off by changing the growth medium. I demonstrate that the model improves on previous approaches both in terms of change point detection and network reconstruction.

This talk is part of the Machine Learning Reading Group @ CUED series.

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