Inferring network structure from interventional time-course experiments

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.

• Publication year

  2015

• Venue

  The Annals of Applied Statistics

• Publication date

  2015-04-28

• Fields of study

  Biology, Mathematics, Computer Science

• Identifiers
  DOI 10.1214/15-AOAS806arXiv 1504.07882
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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