Inferring network structure from interventional time-course experiments. (English) Zbl 1454.62403

Spencer, Simon E. F.; Hill, Steven M.; Mukherjee, Sach

Ann. Appl. Stat. 9, No. 1, 507-524 (2015).

Summary: Graphical models are widely used to study biological networks. Interventions on network nodes are an important feature of many experimental designs for the study of biological networks. In this paper we put forward a causal variant of dynamic Bayesian networks (DBNs) for the purpose of modeling time-course data with interventions. The models inherit the simplicity and computational efficiency of DBNs but allow interventional data to be integrated into network inference. We show empirical results, on both simulated and experimental data, that demonstrate the need to appropriately handle interventions when interventions form part of the design.

MSC:

62P10	Applications of statistics to biology and medical sciences; meta analysis
62F15	Bayesian inference

Keywords:

Bayesian inference; network inference; structure learning; causal inference; dynamic Bayesian network; causal Bayesian network

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